phd in health data science

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Health Data Science - PHD

Program Guide

The PhD Program in Health Data Science trains the next generation of data science leaders for applications in public health and medicine. The program advances future leaders in health and biomedical data science by: (i) providing rigorous training in the fundamentals of health and biomedical data science, (ii) fostering innovative thinking for the design, conduct, analysis, and reporting of public health research studies, and (iii) providing practical training through real-world research opportunities at research centers and institutes directed by departmental faculty such as the Biostatistics Center (BSC), the Computational Biology Institute (CBI), and the Biostatistics and Epidemiology Consulting Service (BECS).

The PhD program consists of two concentrations; Biostatistics & Bioinformatics Concentration. Biostatistics is the science of designing, conducting, analyzing, and reporting studies aimed at advancing public health and medicine. Bioinformatics is the science of developing and applying computational algorithms and analysis methodologies to big biological data such as genetic sequences. Together they are foundational sciences for public health research and decision-making and essential to educating the next generation of leaders in health and biomedical data science.

The program takes advantage of the rich biostatistical and bioinformatics resources at GW and in the Nation’s Capital. Faculty in the Department of Biostatistics and Bioinformatics are engaged in a diverse research portfolio that includes areas such as diabetes, infectious diseases, mental health, maternal-fetal medicine, cardiovascular disease, emergency medicine, and oncology. Methodological interests of the faculty include the design and analyses of clinical trials including group-sequential and adaptive design, SMART trials, pragmatic trials, multiple testing, and benefit: risk evaluation; machine learning; meta-analyses; missing data; randomization tests, longitudinal data; the use of real-world data including electronic medical records; and research in biostatistics education methodologies. The Washington DC area is a hub for biostatisticians and bioinformaticians in government and industry, providing a rich source of adjunct faculty with relevant experience.  Specifically, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) have considerable human resources in these disciplines, many with world-class reputations. Several leading biostatisticians from the NIH are currently serving on doctoral committees and teach courses in the Milken Institute School of Public Health (GWSPH).

The program features a modernized applied curriculum, unique in its emphasis on cutting-edge data science techniques while retaining the rigor of traditional Biostatistics and Bioinformatics programs. The program prepares students to be independent researchers and effective collaborators in interdisciplinary studies.

APPLICATION DEADLINE: DECEMBER 1

Program Co-Directors:  Dr. Keith Crandall  (Bioinformatics Concentration)

Dr. Guoqing Diao  (Biostatistics Concentration)

Dr. Toshimitsu Hamasaki  (Biostatistics Concentration)

GWSPH Doctoral programs admit students for the Fall term each academic year. Applications will be accepted beginning in August and are due no later than December 1st for the next matriculating cohort beginning in the following Fall term.  Find GWSPH graduate admissions information  here .

All applicants for the Biostatistics Concentration are required to submit current GRE scores (within five years of matriculation date). Applicants for the Bioinformatics Concentration are strongly encouraged to submit a GRE score.

Meeting the minimum requirements does not assure acceptance. Applicants must provide evidence of the completion of their undergraduate and/or graduate work before registration in GWSPH is permitted.

Concentration-Specific Prerequisites

Transfer Credits

Graduate courses taken prior to admission while in non-degree status may not be transferable into GWSPH programs. The PhD program is designed to serve students coming directly from an undergraduate degree. Students completing a master’s degree prior to admission to the PhD degree program may be eligible to transfer up to 24 credits toward the PhD coursework requirements. Depending on how many transfer credits are accepted, at minimum, 48 credits of additional coursework and dissertation research will be required.

PUBH 6080  | Pathways to Public Health (0 credits) PUBH 6421 | Responsible Conduct of Research (1 credit) PUBH 6850   | Introduction to SAS for Public Health Research (1 credit) PUBH 6851   | Introduction to R for Public Health Research (1 credit) PUBH 6852   | Introduction to Python for Public Health Research (1 credit)  PUBH 6860   | Principles of Bioinformatics (3 credits)  PUBH 6886   | Statistical and Machine Learning for Public Health Research (3 credits) PUBH 8099 | PhD Seminar: Cross Cutting Concepts in Public Health (1 credit) NOTE: In 23-24, PUBH 8099 was updated to PUBH 8001 PUBH 8870 | Statistical Inference for Public Health Research I* (3 credits)

CORE TOTAL: 14 CREDITS

SPH Course Descriptions

PUBH 6866   | Principles of Clinical Trials (3 credits)  PUBH 6869   | Principles of  Biostatistical Consulting (1 credit) PUBH 6879 | Propensity Score Methods for Causal Inference in Observational Studies (3 credits) PUBH 6887   | Applied Longitudinal Data Analysis for Public Health Research (3 credits) PUBH 8871   | Statistical Inference for Public Health Research II* (3 credits) PUBH 8875 | Linear Models in Biostatistics* (3 credits)  PUBH 8877   | Generalized Linear Models in Biostatistics* (3 credits) PUBH 8878 | Statistical Genetics (3 credits) PUBH 8879 | An Introduction to Causal Inference for Public Health Research (3 credits) PUBH 8880 | Statistical Computing for Public Health Research (3 credits) STAT 6227   | Survival Analysis (3 credits)

BIOSTATISTICS CONCENTRATION-SPECIFIC TOTAL: 28 CREDITS

* Courses are basis of comprehensive exam for the Biostatistics concentration.

PUBH 6854 | Applied Computing in Health Data Science (3 credits) PUBH 6859 | High Performance Cloud Computing (3 credits)  PUBH 6861   |  Public Health Genomics (3 credits)  PUBH 68 84   |  Bioinformatics  Algorithms and Data Structure (3 credits)  PUBH 8885   | Computational Biology (3 credits) 

BIOINFORMATICS CONCENTRATION-SPECIFIC TOTAL: 15 CREDITS

• Applied Biostatistics Concentration: 12 credits minimum
• Bioinformatics Concentration:  18 credits minimum

Both concentrations:  elective selections must include at least*:

• 3 Credits in Biostatistics 
• 3 Credits in Bioinformatics
• 3 Credits in a Cognate Area

All students are expected to work with their Advisor in the selection of their Elective coursework.

*Pre-approved elective courses are shown in the program guide for each category. 

 BIOSTATISTICS ELECTIVES MINIMUM: 12 CREDITS BIOINFORMATICS ELECTIVES MINIMUM: 18 CREDITS

PRACTICUM/TEACHING RESEARCH GTAP**   |   GradTeachingAsst Certification (This includes UNIV 0250 - Graduate Assistant Certification Course (1 credit) (both concentrations) (0; 1 credit) PUBH 8283 | Doctoral Biostatistics Consulting Practicum (Biostatistics concentration only) (2 credits) PUBH 8413 |  Research Leadership (both concentrations (1 credit)

** This is a requirement for TAs .

DISSERTATION RESEARCH PUBH 8999 |  Dissertation Research (varies by concentration - 12 minimum credits)

BIOSTATISTICS PRACTICUM/RESEARCH: 12-15 CREDITS BIOINFORMATICS PRACTICUM/RESEARCH: 12-24 CREDITS

Professional Enhancement

Students in degree programs must participate in eight hours of Professional Enhancement. These activities may be Public Health-related lectures, seminars, or symposia related to your field of study.

Professional Enhancement activities supplement the rigorous academic curriculum of the SPH degree programs and help prepare students to participate actively in the professional community. You can learn more about opportunities for Professional Enhancement via the Milken Institute School of Public Health Listserv, through departmental communications, or by speaking with your advisor.

Students must submit a completed  Professional Enhancement Form  to the student records department  [email protected] .

Collaborative Institutional Training Initiative (CITI) Training

All students are required to complete the Basic  CITI training module  in Social and Behavioral Research prior to beginning the practicum.  This online training module for Social and Behavioral Researchers will help new students demonstrate and maintain sufficient knowledge of the ethical principles and regulatory requirements for protecting human subjects - key for any public health research.

Academic Integrity Quiz

All Milken Institute School of Public Health students are required to review the University’s Code of Academic Integrity and complete the GW Academic Integrity Activity.  This activity must be completed within 2 weeks of matriculation. Information on GWSPH Academic Integrity requirements can be found  here.

Past Program Guides

Students in the PhD in Health and Biomedical Data Science program should refer to the guide from the year in which they matriculated into the program. For the current program guide, click the "PROGRAM GUIDE" button on the right-hand side of the page.

Program Guide 2023-2024 Program Guide 2022-2023 Program Guide 2021-2022

Lizhao (Agnes) Ge Email:   [email protected] Start year: 2021

Lizhao was born and raised in Zhejiang, China. She came to the United States for undergraduate studies at the University of Iowa, where she obtained a BS in Mathematics and a BBA in Finance, and a minor in Music. She earned a Master of Applied Statistics from the Pennsylvania State University and worked there as a Statistical Consultant after graduation. She joined the Antibacterial Resistance Leadership Group (ARLG) at the George Washington University Biostatistics Center as a biostatistician in 2020 and started her PhD journey in the Health and Biomedical Data Science (Applied Biostatistics track) in 2021. Her research interests are clinical trial designs, and application of the Desirability of Outcome Ranking (DOOR) in biomedical studies.

Yijie He Email:   [email protected] Start year: 2021

Yijie was born in China. Before coming to the George Washington University, he received a BS degree in Bioengineering from University of California San Diego and an MS degree in Biostatistics from Duke University. He is currently a PhD student in Health and Biomedical Data Science, in the Applied Biostatistics track, and he also works at the George Washington University Biostatistics Center as a biostatistician. His current research interests include clinical trials, high-dimensional data, and data science.

Shiyu Shu Email:  [email protected] Start year: 2021

Shiyu (Richard) was born and raised in Dalian, China, and has been studying in the United States for the last 7 years. He obtained a BA in Mathematics and in Economics from Vassar College, during which he spent one semester as an exchange student at St Edmund Hall, Oxford University. He then received a Master of Statistical Practice from Carnegie Mellon University, and worked as a data analyst for a healthcare organization in rural Arizona during the peak of the COVID pandemic. The work experience motivated him to pursue a career in public health, and to continue his PhD studies in the Health and Biomedical Data Science program at GWU. He is currently a biostatistician working in the Diabetes Prevention Program team (DPP) at the Biostatistics Center, under the supervision of Dr. Marinella Temprosa. His current research interests include machine learning/data science, genomics data and survival analysis.

Shanshan Zhang Email:  [email protected] Start year: 2021

Shanshan was born and raised in China. She earned a Bachelor of Medicine and a Master of Science in Cell Biology from China Medical University. When she came to the United States in 2018, she transferred her interests to public health, since a doctor can save individuals, whereas a public health expert can save lives on a population level. She obtained a second graduate degree, an MS in Biostatistics from the George Washington University. Shanshan hopes that she can make contributions to the field of public health, especially in designing and conducting clinical trials during the PhD program, and can work as an outstanding biostatistician in the future.

Recent Publications:

Qiongfang Wu, Leizhen Xia, Lifeng Tian, Shanshan Zhang, Jialyu Huang. Hormonal replacement treatment for frozen-thawed embryo transfer with or without GnRH agonist pretreatment: a retrospective cohort study stratified by times of embryo implantation failures. Accepted by Frontiers in Endocrinology. 5 January 2022

Shanshan Zhang. Biostatistics in Clinical Decision Making: What can We Get from a 2× 2 Contingency Table. E3S Web of Conferences (Vol. 233). EDP Sciences. December 2020

Qiqiang Guo, Shanshan Wang, Shanshan Zhang, Hongde Xu, Xiaoman Li, et al. ATM‐CHK 2‐Beclin 1 Axis Promotes Autophagy to Maintain ROS Homeostasis Under Oxidative Stress. The EMBO Journal, 39(10), e103111. 18 March 2020

PhD in Bioinformatics Students

Mahdi Baghbanzadeh Email:  [email protected] ;  [email protected] Start year: 2021

Mahdi Baghbanzadeh is a Ph.D. student in the health and biomedical data science program at the Milken Institute School of Public Health at the George Washington University. Mahdi received his MS in Mathematical Statistics from Shiraz University in 2012, and his BS in Statistics from Shahid Beheshti University in 2010.  Before joining GWU, he had the experience of 7 years performing in an analytical role ranging from data analyst to senior data scientist in multiple companies. His research interests are applying machine learning algorithms in analyzing omics data, developing tools for studying the genotype-phenotype association studies, and the effects of different medications on a certain disease.

Publications:

Baghbanzadeh, Mostafa; Simeone, F. C.; Bowers, C. M.; Liao, K.-C.; Thuo, M. M.;  Baghbanzadeh, Mahdi ; Miller, M.; Carmichael, T. B.; Whitesides, G. M.*  “Odd-even effects in charge transport across n-alkanethiolate-based SAMs”   Journal of American Chemical Society ,  2014 ,  136 , 16919–16925.

Mahdi Baghbanzadeh , Dewesh Kumar, Sare I. Yavasoglu, Sydney Manning, Ahmad Ali Hanafi-Bojd, Hassan Ghasemzadeh, Ifthekar Sikder, Dilip Kumar, Nisha Murmu, Ubydul Haque*  “Malaria Epidemics in India: Role of Climatic Condition and Control Measures”   Science of the Total Environment ,  2020 ,  712 , 136368.

Peeri, Noah C., Nistha Shrestha, Md Siddikur Rahman, Rafdzah Zaki, Zhengqi Tan, Saana Bibi,  Mahdi Baghbanzadeh , Nasrin Aghamohammadi, Wenyi Zhang, and Ubydul Haque.  " The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? "   International Journal of Epidemiology ,  2020 ,  49 , 717-726.

Md Siddikur Rahman, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Miftahuzzannat Amrin, Sumaira Zafar, Nadia Nahrin Rahman, Sharifa Umma Shirina, Ubydul Haque,  “Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media–based cross-sectional survey” ,  Transactions of The Royal Society of Tropical Medicine and Hygiene ,  2021 ,  115 , 85-93.

Nistha Shrestha, Muhammad Yousaf Shad, Osman Ulvi, Modasser Hossain Khan, Ajlina Karamehic-Muratovic, Uyen-Sa D.T. Nguyen,  Mahdi Baghbanzadeh , Robert Wardrup, Nasrin Aghamohammadi, Diana Cervantes, Kh. Md Nahiduzzaman, Rafdzah Ahmed Zaki, Ubydul Haque,  “ The impact of COVID-19 on globalization” ,  One Health ,  2020 , 100180.

Osman Ulvi, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Ateka Bashir, Jacob Smith, Ubydul Haque, “ Social Media Use and Mental Health: A Global Analysis ”,  Epidemiologia,   2022 , 3 (1), 11-25 .

Ranojoy Chatterjee Email:  [email protected] ;  [email protected] Start year: 2021

Ranojoy is originally from Kolkata, India. He got his B.Tech in Computer Science from WBUT and has an MS in Computer Science from Kansas State University, specializing in recommendation systems using a multi-armed bandit approach. After graduation he worked at Bellwethr, Inc developing a retention engine which was later patented by the company. After his brief stint in industry, he worked as a research specialist in Rahlab to develop machine learning tools for analyzing Covid-19 data. His current research interests are graph neural networks, single cell data and prediction systems in biomedical data science.  

Amritphale, A., Chatterjee, R., Chatterjee, S.  et al.  Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.  Adv Ther   38,  2954–2972 (2021).  https://doi.org/10.1007/s12325-021-01709-7

Chow JH, Rahnavard A, Gomberg-Maitland M, Chatterjee R, et al. Association of Early Aspirin Use With In-Hospital Mortality in Patients With Moderate COVID-19.  JAMA Netw Open.  2022;5(3):e223890. doi:10.1001/jamanetworkopen.2022.3890

Clark Gaylord Email:  [email protected] Start year: 2021

After receiving M.S. degrees in Mathematics and Statistics from the University of Virginia and Virginia Tech, respectively, Clark has had a career in information technology, network security, and research computing. Over the last 20 years, Clark has led the design and operation of many research computing and big data research systems, and is a consulting statistician on several research projects. While at Virginia Tech, Clark taught several courses in Statistics, Data Science, and Networking. A PhD candidate in GW's Health and Biomedical Data Science, Bioinformatics Track, Clark is also Director of Research Technology Services in GW IT.

CAAREN:  https://www.caaren.org/clark-gaylord GW High Performance Computing:  https://www.hpc.arc.gwu.edu/

Erika Hubbard Email:  [email protected] Start year: 2021

Erika was born and raised in Fairfax County, Virginia (NOVA) and earned her BSc in Biomedical Engineering with minor concentrations in Applied Mathematics and Engineering Business from the University of Virginia. Upon graduation she went on to intern and work for AMPEL BioSolutions, LLC in Charlottesville, VA, researching autoimmune and inflammatory diseases, primarily systemic lupus erythematosus (SLE). As a dual member of the systems biology and bioinformatics teams at AMPEL she developed an interest in leveraging genomics data to gain insights into mechanisms of autoimmune disease pathogenesis. She continues to work with AMPEL to study lupus and translate findings into novel clinical tools to further precision medicine. 

Hubbard EL, Pisetsky DS, Lipsky PE. Anti-RNP antibodies are associated with the interferon gene signature but not decreased complement levels in SLE. Ann Rheum Dis [Epub ahead of print: 3 Feb 2022]. doi:  https://doi.org/10.1136/annrheumdis-2021-221662

Hubbard EL, Grammer AC, Lipsky PE. Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus. Curr Opin Rheumatol [Internet]. 2021 Nov 1;33(6):579-85. doi:  https://doi.org/10.1097/bor.0000000000000833   

Daamen AR, Bachali P, Owen KA, Kingsmore KM, Hubbard EL, Labonte AC, et al. Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway. Sci Rep [Internet]. 2021 Mar 29;11(1):7052. doi:  https://doi.org/10.1038/s41598-021-86002-x

Hubbard EL, Catalina MD, Heuer S, Bachali P, Geraci NS, et al. Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis. Sci Rep [Internet]. 2020 Oct 15;10(1):17361. doi:  https://doi.org/10.1038/s41598-020-74391-4

Xinyang Zhang Email:  [email protected] ;  [email protected] Start year: 2021

Xinyang was born and raised in Jiangsu, China. Before coming to George Washington University, she obtained her MS in Data Informatics at the University of Southern California, Los Angeles. For now, she started her Ph.D. journey in Health and biomedical data science (Applied Bioinformatics track) and works for the Computational Biology Institute (CBI) as a Research Assistant. Her research interest focuses on microbiome analysis, omics data for the COVID-19, and reference-grade pathogen sequences database construction.  

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Health Data Science

• Entry year 2024 or 2025
• Duration Full time 3 - 4 years, Part time 4 - 7 years

The PhD in Health Data Science provides research training in developing applied informatic and analytic approaches to data within health-related subjects such as medicine and the biomedical, biotechnological, and bioengineering sciences.

You will join the programme with a supervisory panel composed of academics working in health data science more broadly. Throughout the programme, and particularly during your first year, you will be encouraged to engage in training opportunities at Lancaster and elsewhere to develop both your research skills and subject-specific knowledge and abilities. Throughout your studies, you will focus on novel scientific research, developing best practice in interpreting and communicating new scientific methods and findings.

Your department

• Lancaster Medical School Faculty of Health and Medicine
• Telephone +44 (0)1524 592032

Entry requirements

Academic requirements.

2:1 Hons degree (UK or equivalent) in a relevant subject.

We may also consider non-standard applicants, please contact us for information.

If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.

Additional Requirements

As part of your application you will also need to provide a viable research proposal. Guidance for writing a research proposal can be found on our writing a research proposal webpage.

English Language Requirements

We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.

We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 5.5 in each element of the test. We also consider other English language qualifications .

If your score is below our requirements, you may be eligible for one of our pre-sessional English language programmes .

Contact: Admissions Team +44 (0) 1524 592032 or email [email protected]

Fees and funding

The tuition fee for students with home fee status is set in line with the standard fee stipend provided by the UK Research Councils. The fee stipend for 2024/25 has not been set. For reference, the fee stipend for 2023/24 was full-time £4,712.

The international fee for new entrants in 2024/25 is full-time £26,490.

Depending on the nature of the research project, an additional programme cost may be charged. This additional fee will contribute towards the costs incurred on specific research projects. These costs could include purchasing specialist consumables, equipment access charges, fieldwork expenses and payments for transcription/translation services.  Normally any additional charge will not exceed a maximum of £9,720 but this could be increased in exceptional circumstances.

Applicants will be notified of any specific additional programme cost when the offer of a place is made.

General fees and funding information

Additional fees and funding information accordion

There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.

Specific additional costs for studying at Lancaster are listed below.

College fees

Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small College Membership Fee which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.

For students starting in 2024, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2025 have not yet been set.

Computer equipment and internet access

To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.

The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.

For most taught postgraduate applications there is a non-refundable application fee of £40. We cannot consider applications until this fee has been paid, as advised on our online secure payment system. There is no application fee for postgraduate research applications.

For some of our courses you will need to pay a deposit to accept your offer and secure your place. We will let you know in your offer letter if a deposit is required and you will be given a deadline date when this is due to be paid.

The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your fee status .

If you are studying on a programme of more than one year’s duration, tuition fees are reviewed annually and are not fixed for the duration of your studies. Read more about fees in subsequent years .

Scholarships and bursaries

You may be eligible for the following funding opportunities, depending on your fee status and course. You will be automatically considered for our main scholarships and bursaries when you apply, so there's nothing extra that you need to do.

Unfortunately no scholarships and bursaries match your selection, but there are more listed on scholarships and bursaries page.

If you're considering postgraduate research you should look at our funded PhD opportunities .

We also have other, more specialised scholarships and bursaries - such as those for students from specific countries.

Browse Lancaster University's scholarships and bursaries .

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Important Information

The information on this site relates primarily to 2025/2026 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.

The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.

More information on limits to the University’s liability can be found in our legal information .

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We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. View our Charter and other policies .

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Health Sciences Informatics, PhD

School of medicine.

The Ph.D. in Health Sciences Informatics offers the opportunity to participate in ground-breaking research projects in clinical informatics and data science at one of the world’s finest biomedical research institutions. In keeping with the traditions of the Johns Hopkins University and the Johns Hopkins Hospital, the Ph.D. program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health informatics, health IT, and data science. Resources include a highly collaborative clinical faculty committed to research at the patient, provider, and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.    

Areas of research:

• Standard Terminologies
• Precision Medicine Analytics
• Population Health Analytics
• Clinical Decision Support
• Translational Bioinformatics
• Health Information Exchange (HIE)
• Multi-Center Real World Data
• Telemedicine

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program.

Admission Criteria

Applicants with the following types of degrees and qualifications will be considered:

• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies and evidence of scholarly accomplishment; or
• In exceptional circumstances, BA or BS, with relevant technical and quantitative competencies, with some combination of scholarly accomplishment and/or professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.)

Relevant fields include: medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical science, bioengineering and pharmaceutical sciences, and computer and information science. An undergraduate minor or major in information or computer science is highly desirable. Professional work experience in one of these fields is also highly desirable. 

The application is made available online through Johns Hopkins School of Medicine's website . Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15 of the following year. Applications will not be reviewed until they are complete and we have all supporting letters and documentation.

• Curriculum Vitae (including list of peer-reviewed publications and scientific presentations)
• Three Letters of Recommendation
• Statement of Purpose
• Official Transcripts from undergraduate and any graduate studies
• Certification of terminal degree
• You are also encouraged to submit a portfolio of published research, writing samples, and/or samples of website or system development

Please track submission of supporting documentation through the SLATE admissions portal.

If you have questions about your qualifications for this program, please contact [email protected]

Program Requirements

The PhD curriculum will be highly customized based on the student's background and needs. Specific courses and milestones will be developed in partnership with the student's advisor and the PhD Program Director.

The proposed curriculum is founded on four high-level principles:

• Achieving a balance between theory and research, and between breadth and depth of knowledge
• Creating a curriculum around student needs, background, and goals
• Teaching and research excellence
• Modeling professional behavior locally and nationally.

Individualized curriculum plans will be developed to build proficiencies in the following areas:

• Foundations of biomedical informatics: e.g., lifecycle of information systems, decision support
• Information and computer science: e.g., software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); ethnographic methods.
• Implementation sciences: methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods), health economics, evidence-based practice, safety, quality.
• Specific informatics domains: clinical informatics, public health informatics, analytics
• Practical experience: experience in informatics research, experience with health information technology.

Basic Requirements

• "Core" courses
• Student Seminar & Grand Rounds
• Selective and Elective courses
• Mentored Research (in Year 1)
• Qualifying Exam (in Year 2)
• Proposal Defense (in Year 2 or 3)
• Dissertation (Years 2-4)
• Final Dissertation Defense (Year 4)
• Research Ethics

Healthcare Data Science (EPSRC CDT)

• Entry requirements
• Funding and costs

College preference

• How to apply

About the course

The Healthcare Data Science (EPSRC Centre for Doctoral Training) is a four-year doctoral cohort-based training programme offering opportunities for doctoral study in computational statistics, machine learning and data engineering within the context of ethically-responsible health research.

This course is jointly run by a range of Oxford departments including the departments of Computer Science, Statistics, Engineering Science, the Nuffield Department of Medicine, and the Nuffield Department of Population Health.

Course structure

The course begins with a training year, which consists of two terms of intensive training in core data science principles and techniques followed by a third term where you will undertake two eight-week research projects in two of your chosen research areas. One of these projects will usually become the basis of your doctoral research, carried out in the following three years.

During the first year, your day will typically comprise of lectures each morning with practical computational exercises each afternoon.

The taught courses covering core subjects such as computational statistics, machine learning, data engineering, ethics and governance, and health research methodology include the following:

• Software Engineering
• Statistical Methods
• Research Methods
• Machine Learning
• Bayesian Statistics
• Medical Imaging
• Biomedical Image Analysis
• Biomedical Time Series Analysis
• Device and Sensor Data
• Infectious Diseases
• Modelling for Policy Making
• Data Governance
• Data Engineering
• Health Data Quality
• Health Data Standards
• Data-driven Innovation.

In each case, you will develop an understanding of relevant concepts and techniques that is not only enough to enable their application and integration but will also serve as a solid foundation should you choose to pursue research in that area.

Each term of taught modules concludes with an extended, team-based two-week data challenge where you will work in small groups with clinicians and domain experts to address questions using large healthcare datasets.

At the start of the second term you will select from a pool of projects. These projects are proposed by Oxford faculty members but you may also contact faculty members to jointly propose projects. There are always more projects than students, and students are typically matched to, at least, their first choice, but it is not possible to guarantee that you will be able to work with a particular member of staff. 

You will undertake two eight-week placements with research groups within the University. These will provide you with experience of working as part of an active group and the opportunity to explore specific areas before writing a proposal for your doctoral research.

At the end of the summer of the first year, you will normally select one of the two projects to become the basis of your DPhil research.

In years two to four you will carry out individual research on a project within the scope of the programme, specifically the development of novel statistical, machine learning or computational methods with application to health or healthcare data. Training will continue in academic reading, writing and presentation skills, ethics, responsible research and innovation, and career development and planning.

While working on your research project, you will have the opportunity to participate in a range of activities including an ethics placement, four-week external data challenge, seminar series and annual CDT retreats.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Medical Sciences Doctoral Training Centre (MSDTC) and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may be found outside the department.

Teaching on taught modules and subsequent research supervision are provided by leading academics from a range of departments at the University. You will benefit from dual supervision for the duration of your research project; at least one of the members of the supervisory team will have a strong background in core data science.

You will be expected to meet your supervisors on a regular basis. These meetings should take place at least once every two weeks, averaged across the year and agreed by both parties, to discuss your progress.

All modules, data challenges and activities during the taught course component involve some aspect of formal assessment, including written reports, problem solving, and group and individual presentations. At the end of year one, you will submit a short DPhil proposal which will be examined orally by the programme directorate to evaluate your progress and the suitability of the project.

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms as a PRS student you will be expected to apply for transfer of status from Probationer Research Student to DPhil status. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status within ten terms of admission, to show that your work continues to be on track.

Both milestones normally involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

You will be expected to submit a original thesis after, at most, four years from the date of admission.

To be successfully awarded a DPhil in Healthcare Data Science you will need to defend your thesis orally (viva voce) in front of two appointed examiners. 

Graduate destinations

It is expected that graduates will be well placed to take on leading roles in industry, academia and the public sector, including areas where health and health care data is used to direct policy or make decisions about patient care.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a first-class or strong upper second-class undergraduate degree with honours  

The above qualification should be achieved in one of the following subject areas of disciplines:

• Mathematics
• Engineering Science
• Computer Science; or
• A related field with substantial mathematical background

A master's qualification in one of the above subjects is recommended, but not essential.

For applicants with a degree from the USA, usually the minimum GPA sought is 3.5 out of 4.0. 

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience

• Research or working experience in a relevant field may be an advantage.
• Whilst not required, or expected, publications demonstrating previous research experience in a relevant field and a track record demonstrating an interest in research are likely to advantage your application.

English language proficiency

This course requires proficiency in English at the University's  higher level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's higher level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides  further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are normally held as part of the admissions process and are expected to take place around a month after the application deadline.

Interviews are usually held remotely and are approximately 30 minutes in length. The interview takes the form of a series of questions to assess readiness to study, specifically your foundational mathematical, statistical and computational skills, and your interest in working at the interface between machine learning and health and healthcare data. 

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Initiatives to improve access to graduate study

This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly.

For this course, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process.  Further information about how we use your socio-economic data  can be found in our page about initiatives to improve access to graduate study.

This is also one of the courses participating in the  Academic Futures programme , including the  Black Academic Futures programme . Applicants who are offered a place on this course and meet the eligibility criteria will subsequently be considered for funding through the Academic Futures programme.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

Academic Technology Approval Scheme (ATAS)

Some postgraduate research students in science, engineering and technology subjects will need an Academic Technology Approval Scheme (ATAS) certificate prior to applying for a  Student visa (under the Student Route) . For some courses, the requirement to apply for an ATAS certificate may depend on your research area.

The Healthcare Data Science cohort-based training programme is based in the Oxford's  Big Data Institute  (BDI), a new purpose-built 7,500 square-metre research institute at the heart of the University's biomedical campus. The institute is an analytical hub for multi-disciplinary working at Oxford, connecting world-leading expertise in statistics, computer science, and engineering to data- driven research in medicine and population health.

The institute has dedicated teaching spaces for classes, workshops, group exercises, and presentations, as well as study space for students during their first year. The institute has many large and small meeting rooms, a large café, and an open, furnished atrium, affording space for formal and informal interaction with research groups, other programmes, and partner organisations. You will have access to a secure research computing infrastructure that supports containerised processing, and you will be able to push your own applications to cloud infrastructure provided by partner organisations. There is central support for common applications and services, including a JupyterHub server for Jupyter notebooks.

The institute houses internationally recognised research groups in genomic medicine, medical image analysis, mobile and sensor data, infectious diseases, and large-scale clinical trials. It is also home to the Ethox Centre  and the newly established Wellcome Centre for Ethics and Humanities .

The BDI hosts the clinical informatics and big data activity of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), a substantial programme (£114m) of translational research, delivered by the University in partnership with Oxford University Hospitals (OUH) NHS Foundation Trust (FT). This activity includes the development of a secure data warehousing and analytics infrastructure - a ‘research platform’ - to support the large-scale re-use of routinely- collected clinical data for research purposes.

The platform contains integrated, longitudinal records for two million patients, including data from patient administration, electronic prescribing, laboratory tests, imaging reports, pathology reports, discharge summaries and clinical letters. It also contains historical datasets, including a comprehensive collection of laboratory test data, on a larger patient population, from 1993 to date. Oxford University Hospitals have agreed to provide students with access to the platform, and to extracts of the data, for approved training and research purposes.

The BDI hosts the informatics activity of the UK Biobank, a major national and international resource for health research. The Biobank team are leading the development of tools for the acquisition, processing, analysis, and re-use of data from clinical and online assessments, imaging, sensors, genotyping, and national datasets (including hospital episodes, death, and primary care) for a cohort of 500,000 participants. CDT students will have the opportunity to access the expertise of the team, and to become involved in Biobank-based research.

Oxford is one of six substantive sites for Health Data Research (HDR) UK . The Oxford HDR UK team, based in the BDI, will lead research initiatives on 21st Century Clinical Trials and Enhancing Prospective Cohort Studies. This work will include the development of new methods and tools for phenotyping at scale, including machine learning approaches to the analysis of large, complex clinical datasets.

When you move out to your DPhil research department you will also have access to the facilities provided by that department. You will remain a member of the CDT and will retain access to the Big Data Institute.

Medical Sciences Doctoral Training Centre

The Medical Sciences Doctoral Training Centre (MSDTC) accommodates the interdisciplinary, cross-departmental DPhil programmes in medical sciences.

Most are structured DPhil programmes, which provide students with the opportunity to undertake two or three 'rotation' projects and relevant course work in their first year of each four-year structured programme. The main doctoral project starts in the second year of each programme. Most of our programmes receive external core-funding, and currently from the Wellcome Trust (WT), British Heart Foundation, Cancer Research UK and EPSRC.

The MSDTC also accommodates the NIH Oxford-Cambridge Scholars’ Programme, the DPhil in Cancer Science programme funded by CRUK which welcomes applications from clinicians, basic scientists, and medical undergraduates, and the new DPhil in Inflammatory and Musculoskeletal Disease which is funded by the Kennedy Trust for Rheumatology Research and is open to Oxford University medical students wishing to undertake DPhils in the fields of musculoskeletal disease, inflammation and immunology.

The department also offers an exciting new programme (the DPhil in Advanced Bioscience of Viral Products) run in collaboration with Oxford Biomedica, which aims to deliver the next generation of bioscience leaders to advance research on the underpinning bioscience of viral products for future gene therapies and vaccines.

Each programme has a distinctive intellectual flavour, designed to nurture independent and creative scientists. Students are supported in their development through:

• supervision and mentoring by world-class academics training in a wide range of research techniques
• development of student resilience and maintenance of mental health and wellbeing from the start and throughout each programme.

View all courses   View taught courses View research courses

We expect that the majority of applicants who are offered a place on this course will also be offered a fully-funded scholarship specific to this course, covering course fees for the duration of their course and a living stipend.

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Annual fees for entry in 2024-25

IMPORTANT : Please note that while most of the content of these pages relates to the course starting in 2024-25, this information about course fees and the additional information section on this page relate to entry in 2025-26 . The remaining content will be updated for 2025-26 entry later in September.

Information about course fees

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

If you are a current Oxford student and you would like to remain at your current Oxford college, you should check whether it is listed below. If it is, you should indicate this preference when you apply. If not, you should contact your college office to ask whether they would be willing to make an exception. Further information about staying at your current college can be found in our Application Guide. 

The following colleges accept students on the Healthcare Data Science (EPSRC CDT):

• Brasenose College
• Exeter College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Lady Margaret Hall
• Linacre College
• Mansfield College
• Reuben College
• St Anne's College
• St Cross College
• St Edmund Hall
• Wolfson College
• Worcester College

Before you apply

Our  guide to getting started  provides general advice on how to prepare for and start your application. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines and when to apply  in our Application Guide.

Application fee waivers

An application fee of £75 is payable for each application to this course. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Application fee waivers for eligible associated courses

If you apply to this course and up to two eligible associated courses from our predefined list during the same cycle, you can request an application fee waiver so that you only need to pay one application fee.

The list of eligible associated courses may be updated as new courses are opened. Please check the list regularly, especially if you are applying to a course that has recently opened to accept applications.

Do I need to contact anyone before I apply?

You do not need to make contact with the department before you apply but you are encouraged to visit the relevant departmental webpages to read any further information about your chosen course.

You may wish to make informal enquiries with the HDS team before you apply in order to work out whether this is the right course for you, and the likely availability of funding. You should do so via the contact details provided on this page.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

Proposed field and title of research project

As you will not choose your research area until the end of year one, you do not need to specify a research field, or project title beyond "HDS cohort-based training programme" in your application. You should not use this field to type out a full research proposal. You will be able to upload your research supporting materials separately if they are required (as described below).

Proposed supervisor

As you will not choose your research supervisor until the end of year one, you do not need to specify a supervisor beyond "HDS cohort-based training programme" in your application.

Referees: Three overall, of which at least two must be academic 

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Academic references are preferred, although a maximum of one professional reference is acceptable where you have completed an industrial placement or worked in a full-time position.

Your references will support your intellectual ability, your academic achievement, your motivation and interest in the course and the subject area, and your ability to work both in a group and independently.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Statement of purpose/personal statement: A maximum of 500 words

You should provide a statement of your research interests, in English, describing how your background and research interests relate to the programme. If possible, please ensure that the word count is clearly displayed on the document.

It will be normal for students’ ideas and goals to change in some ways as they undertake their studies, but your personal statement will enable you to demonstrate your current interests and aspirations.

The statement should focus on academic or research-related achievements and interests rather than personal achievements and interests.

This will be assessed for:

• your reasons for applying;
• evidence of motivation for and understanding of the proposed area of study;
• the ability to present a reasoned case in English;
• capacity for sustained and focused work; and
• understanding of problems in the area and ability to construct and defend an argument.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice .

Application Guide   Apply

ADMISSION STATUS

Closed to applications for entry in 2024-25

Register to be notified via email when the next application cycle opens (for entry in 2025-26)

12:00 midday UK time on:

Tuesday 3 December 2024

Latest deadline for most Oxford scholarships Applications may remain open after this deadline if places are still available - see below

A later deadline under 'Admission status'

If places are still available,  applications may be accepted after 3 December . The Admission status (above) will provide notice of any later deadline.

*Three-year average (applications for entry in 2020-21 to 2022-23)

This course was previously known as Health Data Science

Further information and enquiries

This course is offered jointly by the Big Data Institute and the  Medical Sciences Doctoral Training Centre .

• Course page on the institute's website
• Academic and research staff
• Research in the institute
• Medical Sciences Graduate School
• Mathematical, Physical and Life Sciences
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected]

Application-process enquiries

See the application guide

Other courses to consider

You may also wish to consider applying to other courses that are similar or related to this course:

View related courses

Biomedical Data Science Graduate Program Overview

The Biomedical Data Science Training Program is an interdisciplinary graduate and postdoctoral training program, part of the Department of Biomedical Data Science at Stanford University’s School of Medicine.

Our Mission

History of our graduate program, employment in biomedical data science, directions to dbds, contact information, our educational mission.

The mission of DBDS is to train the next generation of research leaders in Biomedical Data Science. Our students gain knowledge of the scholarly informatics literature and the application requirements of specific areas within biology and medicine. They learn to design and implement novel methods that are generalizable to a defined class of problems, focusing on the acquisition, representation, retrieval, and analysis of biomedical information. We also require training in understanding ethical, social, and legal issues and consequences of research. We seek to attract diverse candidates from all backgrounds and experiences.

What is Biomedical Data Science?

Biomedical Data Science is a broad term comprising multiple areas.

• Bioinformatics develops novel methods for problems in basic biology.
• Translational Bioinformatics moves developments in our understanding of disease from basic research to clinical care.
• Clinical Informatics develops methods and tools directly applied to patient care.
• Public Health Informatics works on challenging problems from health systems and populations.
• Imaging Informatics addresses intelligent management, interpretation, and annotation of biomedical images.

Take a look at our current courses. 

Our Graduate Degrees

The graduate training program offers the PhD degree, and three MS degrees (an academic research-oriented degree, a professional distance-learning masters for part-time students, and co-terminal for Stanford undergraduates). We also have post-doctoral fellows, and offer a distance learning certificate.

• Prerequisites . For a graduate degree, Stanford University requires the applicant to have a bachelor’s degree. We do not require any particular major, but we do require that students have strong undergraduate preparation in computer science/software engineering, mathematics (especially calculus, probability and statistics, and linear algebra), and college-level biology. Applicants with limited backgrounds in these areas should fill the deficiencies prior to applying to our program.
• Curriculum . MS and PhD candidates take coursework in four areas: (1) core DBDS classes, (2) an individual plan with electives in computer science, statistics, mathematics, engineering, and allied informatics-related disciplines, (3) required coursework in social, legal, and ethical issues, (4) unrestricted electives. In addition, PhD candidates are required to choose electives in some area of biology or medicine. Degree candidates also learn important didactic skills by serving as teaching assistants in our core courses.
• Funding . We have been continuously funded by  a training grant from the National Library of Medicine since 1984, which provides fellowship support for students who are US citizens and permanent residents. International students bring outside funding or compete for Stanford Graduate Fellowships. Senior graduate students typically receive funding support through their research supervisor.

The History of Our Graduate Program

History at Stanford

The Biomedical Data Science Graduate Program has a long history both at Stanford and internationally, as the first program of its kind. The degree program was initiated in October 1982 as Medical Information Sciences (MIS) and continues to emphasize interdisciplinary education between medicine, computer science, and statistics, offering pre- and postdoctoral degrees and training. The DBDS Program has been supported by a training grant from the National Library of Medicine since 1984, which initially funded only postdoctoral trainees but was broadened to include predoctoral trainees in 1987. The NLM training grant has been renewed every five years since and has provided tuition and stipend support for hundreds of trainees.

Today, the Biomedical Data Science Graduate Program sits in the newly formed Department of Biomedical Data Science and emphasizes methods development and application across the entire spectrum of biology, medicine, and human health.

A Foundation in Medicine and Computer Science

The interaction between Computer Science and other disciplines has produced vibrant areas of research and education at Stanford since the late 1960s; computing activities in the School of Medicine were stimulated even earlier, principally by the Chair of Genetics, Nobel Laureate Joshua Lederberg. Professor Lederberg collaborated with Professor Carl Djerassi (Chemistry) and Professor Edward Feigenbaum (Computer Science) to create what is arguably the first research program that applied the nascent field of artificial intelligence to biomedical problems. Their U.S. Dendral system, which studied the expertise of mass spectroscopists who could interpret an organic compound’s mass spectrum to infer the chemical structure of that compound, is considered the first expert system.

Professor Lederberg’s second key effort was to attract NIH funding for a large medically focused shared computer for the medical school. This computer, known as ACME, was heavily used by Stanford medical researchers, educators, and students until 1973. It brought a computing culture into the environment, which in turn began to attract medical students who had an interest in the intersection of the two fields.  Later ACME gave way to the SUMEX-AIM Computer, also funded by NIH with Lederberg as PI. This resource was the first biomedically focused machine on the ARPANet, which evolved to become today’s Internet.  The SUMEX Computer was a key resource at Stanford for almost 20 years.

Working closely with Stanley Cohen (a Professor of Medicine who later succeeded Lederberg as Chair of Genetics) and Bruce Buchanan (a research scientist in computer science who was a member of the Dendral Project), Edward Shortliffe undertook a combined MD/PhD with the doctoral degree in a self-designed interdisciplinary program. Further discussion with faculty, students, and researchers emphasized the interest and need to formalize this kind of interdisciplinary education, directly leading to the formation of the MIS graduate program.

The Human Genome Project and a Turn at the Turn of the Century

The launch of the Human Genome Project in 1990 and its completion in 2003 seeded substantial interest and need for computing in the biological community. In 2000 Dr. Russ B. Altman succeeded Dr. Shortliffe as Director of the MIS Program and in recognition of a new mission beyond clinical informatics, to fundamental issues of biomedical knowledge, its representation and its application, the program was renamed Biomedical Data Science  Training Program  (DBDS). The term Biomedical Data Science   represents not only the continued development of medical information systems but also the use of sophisticated computation to study medicine at the molecular, cellular, organismal, and population levels.

Biomedical Data Science Today

On September 1, 2023, the Biomedical Informatics (BMI) training program finalized its last step in merging with the Department of Biomedical Data Science (DBDS) and formally changed its name to the Biomedical Data Science Training Program.

Our trainees admitted after September 1, 2023 will earn their Master’s and PhD degrees in Biomedical Data Science.

The mission of our department and the training program remain fully aligned to “advance precision health by leveraging large, complex, multi-scale real-world data through the development and implementation of novel analytical tools and methods.” Aligning the name of the degree program with department name was widely regarded as both logical and appropriate. More importantly, it reflects a shared vision in our research and education missions that serves to pull our integrated work in biomedical informatics, biostatistics and AI/ML under a unified interdisciplinary umbrella.

The DBDS Training Program at Stanford continues to evolve to meet the needs of biomedical computation and application. Under the guidance of the current Director since 2018 and Chair of the Department of Biomedical Data Science, Professor Sylvia Plevritis, and with support from NLM, the DBDS Program continues to innovate in the areas of Healthcare and Clinical Informatics, Translational Bioinformatics, and Clinical Research Informatics. In addition to historical research thrusts in biomedical knowledge representation and the genetic basis of disease, current research explores algorithms for real world biomedical data, multi-modal data and meta-analysis, medical image analysis, responsible clinical decision making, reproducibility, methods for efficient querying and access to big biomedical data, and more.

Prospective students with interest in career directions in Biomedical Data Science should review a list of our Alumni and their current jobs under the People Directory .

If you have a job posting that you would like to send to the DBDS students and post-docs, please email it to dbds-job-openings at lists.stanford.edu for distribution as we deem appropriate for our audience.

DBDS Current Students and Alumni

The  School of Medicine Career Center  offers resources for professional and leadership development, resources for the job hunt ranging from presentation skills, resume preparation, interview skills to job hunt strategy. There is a seminar series from both industry and academia, and a number of industry events: demos, job fairs, industry mixers.

The University’s  Career Development Center  supports undergraduate and graduate career development. They have  Career Fairs .

To add your name to the DBDS jobs email list, send your request to the DBDS student services team .

External Job Listings in Biomedical Data Science

AMIA Job Exchange BayBio’s Job Sites list BioCareer’s Job site Bioinformatics.org’s Jobs site BioinformaticsDirectory listings Genomeweb’s Job listings ISCB Jobs Database Nature’s Jobs list New Scientist Jobs NIH’s job listings Science Career’s Ziprecruiter

Postdoctoral Positions at Stanford

Please see the descriptions for various opportunities in Biomedical Data Science under Postdoctoral Training

Directions to DBDS Program Offices

The DBDS Program Offices are in the Stanford’s Medical School Office Building (MSOB). The street address is: 1265 Welch Road, Stanford, CA 94305.

MSOB is located on the corner of Campus Drive West and Welch Road, between Panama Street and Welch Road. MSOB is a three story white building with redwood window framing. The exact latitude/longitude is 37.431734, -122.179476. See the map, below.

There are two options for parking:

• The parking lot in front of our building, which has an entrance on Welch Road. This lot has a few parking spots with coin metered parking.
• The large parking lot across the street on Welch Road. Entrance to the lot is from Stock Farm Road or Oak Road, but you have to drive within the lot towards the corner of Welch Road and Campus Drive. Payment is through cash, coins, or credit card using an automated permit dispenser. Information:  https://transportation.stanford.edu/parking

For all questions about the program, email: 

[email protected]

Mailing Address: Office Location 

Department of Biomedical Data Science Graduate Training Program

Stanford University School of Medicine

1265 Welch Road, MSOB X-343

Stanford, CA 94305-5464

Health Data Science

The Health Data Science (HDS) area of study provides students with a blend of strong statistical and computational skills needed to manage and analyze health science data in order to address important questions in public health and biomedical sciences. This training will enable students to manage and analyze massive, noisy data sets and learn how to interpret their findings. The program will provide training in three principal pillars of health data science: statistics, computing, and health sciences.  

The Master of Science (60-credits) in HDS is designed to be a terminal professional degree, giving students essential skills for the job market. HDS also provides a strong foundation for students interested in obtaining a PhD in biostatistics or other quantitative or computational science with an emphasis in data science and its applications in health science.  

Department overview

The Department of Biostatistics offers an unparalleled environment to pursue research and education in statistical science while being at the forefront of efforts to benefit the health of populations worldwide.    

Degree programs

The 60-credit Master of Science degree is designed for professionals with bachelor’s degrees dedicated to public health research in biostatistics and health data sciences.  

• Abbreviation: SM-60  
• Degree format: On campus  
• Time commitment: Full-time or part-time  
• Average program length: 1.5 years full-time, three years part-time  

Student interests

Students who choose the Health Data Science (HDS) area of study are interested in visualizing and interpreting data, as well as effectively communicating results and findings. HDS students will learn to apply methods for big data to reveal patterns, trends, and associations.  

Career outcomes

The Master of Science (60-credits) HDS candidates receive a well-rounded curriculum that can be applied across a variety of disciplines for career growth.  

Graduates of the SM-60 HDS program have found employment as:  

• Research analysts at consulting firms  
• Program coordinators at community-based organizations  
• Project directors at city, state, and federal health commissions  
• Leaders at non-profit organizations  

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Faculty of Interest

Professor of Biomedical Informatics & Data Science; Vice Chair for Education, Biomedical Informatics & Data Science; Professor, Biostatistics

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Associate Professor of Biostatistics, Associate Professor of Ecology and Evolutionary Biology, Associate Professor of Management, and Associate Professor of Statistics and Data Science

Department Chair and Professor of Biostatistics; Affiliated Faculty, Yale Institute for Global Health; Director, Biostatistics and Bioinformatics Shared Resource

Assistant Professor of Biostatistics (Health Informatics)

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Assistant Professor of Biostatistics; Co-Training Director, Health Informatics MS

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Elihu Professor of Biostatistics and Professor of Ecology and Evolutionary Biology; Co-Leader, Genomics, Genetics, & Epigenetics Research Program

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Associate Professor of Biostatistics; Associate Professor, Biomedical Informatics & Data Science

Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science; Affiliated Faculty, Yale Institute for Global Health

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The PhD program is designed for students seeking the highest level of advanced training in the area of health informatics.

Students take a sequence of core courses in health informatics, computing, and biostatistics, and electives in technical and health science areas, and pursue one of four tracks: Data Science and Informatics for Learning Health Systems ; Clinical Informatics ; Translational Bioinformatics ; or Precision and Personalized Medicine (PPM) Informatics . Students pursuing the Data Science and Informatics for Learning Health Systems track are expected to complete the University’s Data Science MS degree en route to the PhD.

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Onsite | Full-Time | 5 years

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About the PhD in Biostatistics Program

The PhD in Biostatistics provides training in the theory of probability and statistics in biostatistical methodology. The program is unique in its emphasis on the foundations of statistical reasoning and data science. Students complete rigorous training in real analysis-based probability and statistics, equivalent to what is provided in most departments of mathematical statistics and in advanced data science.

PhD candidates are required to pass a comprehensive written examination covering coursework completed at the end of their first year. Research leading to a thesis may involve development of new theory and methodology, or it may be concerned with applications of statistics and probability to problems in public health, medicine or biology.

Application Fee Waivers: We are able to offer a limited number of application fee waivers. Learn about the eligibility criteria and how to apply for a waiver .

PhD in Biostatistics Program Highlights

Conduct and publish original research.

on the theory and methodology of biostatistics

Apply innovative theory and methods

to the solution of public health problems

Serve as an expert biostatistician

on collaborative teams of investigators addressing key public health questions

Teach biostatistics effectively

to health professionals and scientists as well as to graduate students in biostatistics

What Can You Do With a PhD In Biostatistics?

Visit the Graduate Employment Outcomes Dashboard to learn about Bloomberg School graduates' employment status, sector, and salaries. We have over 750 global alumni working in academia, government, and industry.

Sample Careers and Next Steps

• Tenure Track Faculty (e.g. Assistant Professor)
• Postdoctoral Fellow
• Data Scientist
• Statistician
• Biostatistician
• Machine Learning Engineer
• Mathematical Statistician
• Principal Investigator

Curriculum for the PhD in Biostatistics

Browse an overview of the requirements for this PhD program in the JHU  Academic Catalogue  and explore all course offerings in the Bloomberg School  Course Directory .

Admissions Requirements

For general admissions requirements, please visit the How to Apply page. This specific program also requires:

Prior Coursework

Calculus and linear algebra; accepted applicants are also strongly encouraged to take real analysis before matriculating

Standardized Test Scores

Standardized test scores are  not required and not reviewed  for this program. If you have taken a standardized test such as the GRE, GMAT, or MCAT and want to submit your scores, please note that they will not be used as a metric during the application review.  Applications will be reviewed holistically based on all required application components.

Vivien Thomas Scholars Initiative

The  Vivien Thomas Scholars Initiative (VTSI)  is an endowed fellowship program at Johns Hopkins for PhD students in STEM fields. It provides full tuition, stipend, and benefits while also providing targeted mentoring, networking, community, and professional development opportunities. Students who have attended a historically Black college and university (HBCU) or other minority serving institution (MSI) for undergraduate study are eligible to apply. To be considered for the VTSI, you will need to submit a SOPHAS application ,VTSI supplementary materials, and all supporting documents (letters, transcripts, and test scores) by December 1, 2024. VTSI applicants are eligible for an  application fee waiver , but the fee waiver must be requested by November 15, 2024 and prior to submission of the SOPHAS application.

Per the Collective Bargaining Agreement (CBA) with the JHU PhD Union, the minimum guaranteed 2025-2026 academic year stipend is $50,000 for all PhD students with a 4% increase the following year. Tuition, fees, and medical benefits are provided, including health insurance premiums for PhD student’s children and spouses of international students, depending on visa type. The minimum stipend and tuition coverage is guaranteed for at least the first four years of a BSPH PhD program; specific amounts and the number of years supported, as well as work expectations related to that stipend will vary across departments and funding source. Please refer to the CBA to review specific benefits, compensation, and other terms.

Need-Based Relocation Grants

Students who  are admitted to PhD programs at JHU starting in Fall 2023 or beyond can apply to receive a need-based grant to offset the costs of relocating to be able to attend JHU.   These grants provide funding to a portion of incoming students who, without this money, may otherwise not be able to afford to relocate to JHU for their PhD program. This is not a merit-based grant. Applications will be evaluated solely based on financial need.  View more information about the need-based relocation grants for PhD students .

Questions about the program? We're happy to help. 

Academic Administrator Mary Joy Argo 410-614-4454 [email protected]

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Structured PhD Program in Health Data Sciences

The PhD Program in Health Data Sciences at the Charité is hosted in English and aimed at qualified young scientists interested in:

• deepening their methodological knowledge in the fields of biostatistics, epidemiology, public health, meta-research, population health science and medical informatics.
• further expanding their competence in research and teaching.

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• PhD in Health Data Sciences

PhD Program in Health Data Sciences

Upon successful completion of the program, students will be awarded the academic degree of "Doctor of Philosophy" (PhD).

We are no longer accepting applications for entrance into our October 2024 cohort.

The deadline to submit applications for entrance into the October 2024 HDS PhD cohort was February 29, 2024 (23:59 CET).

We plan to host an informational session for prospective applicants in the fall of 2024. The application window for our October 2025 HDS PhD Cohort will open in the beginning of 2025. Please check back in the autumn of 2024 for further details. 

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Population and Health Data Science, Ph.D.

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Top 5 for overall research quality (ref2021), key course details.

Course Overview

Entry Points: September, January, April, July

Our Population and Health Data Science PhD programme is available on a full-time or part-time basis, over 3 or 6 years. 

The growing pressures on healthcare systems globally are well documented, with chronic diseases, ageing populations and increased incidents of mental health. Advancements in medicine are improving health for more and more people, but inequalities in the healthcare received by people based on where they are born, live and work are prevalent.  Population health aims to address these inequalities, by working to better understand the healthcare needs of groups of people, improving healthcare models and providing innovative solutions to meet people’s health needs. Additionally, healthcare already has an established strong relationship with Information and Communication Technologies (ICT), and is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.

Your programme will feature:

• Practice-focused learning
• Supervision team with nominated supervisors
• You will draw on skills from the broader academic community
• Programme seminars and workshops
• Access to Wales’ premier purpose-build medical research facility
• Collaboration with industry and business partners

During your course, you will benefit from our highly regarded specialist facilities. Our vision is to advance medical science through interdisciplinary research and innovation. We are a leading centre for medical research and a unique example of successful collaboration between NHS, academia and industry in the life science and health sector.

We have strong connections with a range of external partners and collaborators, including the NHS;  Swansea Bay University Health Board (SBUHB) ;  Hywel Dda University Health Board ; UK research council; Welsh Government and numerous national and international links. These connections, along with the skills and qualities you develop during your research degree will enhance your CV, and help you stand out in a highly competitive graduate employment market

Entry Requirements

Qualifications MPhil:  Applicants for MPhil must normally hold an undergraduate degree at 2.1 level (or Non-UK equivalent as defined by Swansea University). See -  Country-specific Information for European Applicants 2019  and  Country-specific Information for International Applicants 2019 .

PhD : Applicants for PhD must normally hold an undergraduate degree at 2.1 level and a master’s degree. Alternatively, applicants with a UK first class honours degree (or Non-UK equivalent as defined by Swansea University) not holding a master’s degree, will be considered on an individual basis. See -  Country-specific Information for European Applicants 2019  and  Country-specific Information for International Applicants 2019 . 

English Language IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University recognised equivalent.  Full details of our English Language policy, including certificate time validity, can be found here.

As well as academic qualifications, Admissions decisions may be based on other factors, including (but not limited to): the standard of the research synopsis/proposal, performance at interview, intensity of competition for limited places, and relevant professional experience.

Reference Requirement

As standard, two references are required before we can progress applications to the College/School research programme Admissions Tutor for consideration.

Applications received without two references attached are placed on hold, pending receipt of the outstanding reference(s). Please note that any protracted delay in receiving the outstanding reference(s) may result in the need to defer your application to a later potential start point/entry month, than what you initially listed as your preferred start option.

You may wish to consider contacting your referee(s) to assist in the process of obtaining the outstanding reference(s) or alternatively, hold submission of application until references are sourced. Please note that it is not the responsibility of the University Admissions Office to obtain missing reference(s) after our initial email is sent to your nominated referee(s), requesting a reference(s) on your behalf.

The reference can take the form of a letter on official headed paper, or via the University’s standard reference form. Click this link to download the university reference form .

Alternatively, referees can email a reference from their employment email account, please note that references received via private email accounts, (i.e. Hotmail, Yahoo, Gmail) cannot be accepted.

References can be submitted to [email protected] .

How you are Supervised

You will be given a supervisory team made up of a primary supervisor and secondary supervisors. This team will provide both academic, and pastoral support whilst you complete your research.

Welsh Provision

Tuition fees.

Tuition fees for years of study after your first year are subject to an increase of 3%.

You can find further information of your fee costs on our tuition fees page .

You may be eligible for funding to help support your study. To find out about scholarships, bursaries and other funding opportunities that are available please visit the University's scholarships and bursaries page .

Current students: You can find further information of your fee costs on our tuition fees page .

Funding and Scholarships

You may be eligible for funding to help support your study.

Government funding is now available for Welsh, English and EU students starting eligible postgraduate research programmes at Swansea University. To find out more, please visit our postgraduate loans page.

To find out about scholarships, bursaries and other funding opportunities that are available please visit the University's scholarships and bursaries page.

Academi Hywel Teifi at Swansea University and the Coleg Cymraeg Cenedlaethol offer a number of generous scholarships and bursaries for students who wish to study through the medium of Welsh or bilingually. For further information about the opportunities available to you, visit the Academi Hywel Teifi Scholarships and Bursaries page.

Additional Costs

Access to your own digital device/the appropriate IT kit will be essential during your time studying at Swansea University. Access to wifi in your accommodation will also be essential to allow you to fully engage with your programme. See our dedicated webpages for further guidance on suitable devices to purchase, and for a full guide on getting your device set up .

You may face additional costs while at university, including (but not limited to):

• Travel to and from campus
• Printing, photocopying, binding, stationery and equipment costs (e.g. USB sticks)
• Purchase of books or texts
• Gowns for graduation ceremonies

How to Apply

Full details of the research degree application process are available  here , and you can  apply online and track your application status here . As part of your application please include a research proposal outlining your proposed topic of study.  Guidance on writing a research proposal is also available .

You can expect to be interviewed following your application to discuss your topic of research and to demonstrate the necessary level of commitment to your studies and training.

It is advisable that you contact us at  [email protected]   before submitting your application. This will ensure we can identify appropriate supervisors, and work with you to refine your proposal.

If you're an international student, find out more at our  international student web pages .

Suggested Application Timings

In order to allow sufficient time for consideration of your application by an academic, for potential offer conditions to be met and travel / relocation, we recommend that applications are made before the dates outlined below. Please note that applications can still be submitted outside of the suggested dates below but there is the potential that your application/potential offer may need to be moved to the next appropriate intake window.

October Enrolment

UK Applicants – 15th August

EU/International applicants – 15th July

January Enrolment

UK applicants – 15th November

EU/International applicants – 15th October

April Enrolment

UK applicants – 15th February

EU/International applicants – 15th January

July Enrolment

UK applicants – 15th May

EU/International applicants – 15th April

EU students - visa and immigration information is available and will be regularly updated on our information for EU students page.

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PhD Program in Data Sciences for Global Health

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The PhD program in Data Sciences for Global Health, jointly offered by BITS Pilani and the One Health Trust (OHT), provides full-time, advanced education in global health and data sciences. This program offers training on global health plus qualitative and quantitative skills, with rigorous fieldwork.

Students are advised by experts with experience in infectious disease dynamics, antimicrobial resistance, vaccines and immunization, data sciences, environmental health, gender equity and livelihoods, health and development, health systems, and economics.

Doctoral candidates spend part of their tenure at BITS Pilani campus and the other part at OHT’s headquarters.

Questions? Please contact:

• The OHT team at  [email protected]
• The BITS Pilani team at  [email protected] .

The next application cycle will begin in September/October 2024!

The link to the application portal will be available soon.

Each academic year has two semesters and is structured as follows:

Year One (first and second semester)

The students must complete six courses, covering three main subjects (totalling 24 credits):

• Global Health
• Data Sciences
• Health Economics and Policy

The coursework for the first year will help students to build a strong theoretical foundation in global health and equip them with skills in data sciences.

The first year will be conducted at BITS Pilani.

A qualifying exam will be held at the end of the first year and will be jointly by BITS Pilani and OHT.  The qualifying exam will align with the coursework and will include a written test and viva. Students will be promoted to the second year only if they pass in at least two of their main subjects. The students must maintain a minimum grade of D and CGPA of 5.50 throughout all semesters.

Year two (third and fourth semester)

The second year will be conducted at BITS Pilani and OHT. Students are strongly encouraged to find potential advisor(s). Their main advisor will be from BITS Pilani, while the co-advisor will be from OHT. Students will also choose a two-member doctoral advisory committee (DAC) from the faculty members of BITS and OHT.

Students will draft a detailed research proposal to undertake thesis work and submit to their advisory committee for review. The students should take independent study/classes from faculty members from BITS Pilani or OHT, in their research areas of interest (directed individual study), as they work on their research proposal and papers.

A rotation method can be used to learn from various faculty members. Students are also encouraged to learn grant writing from their notional supervisor(s) and apply for research grants. At the end of each semester, students are expected to submit term papers based on their research.

A candidacy/oral exam will be held at the end of the second year. The exam will include an oral presentation on the research proposal developed throughout the year. The proposal will be defended in the presence of a peer group and faculty of the concerned departments. Following the approval, the students can register their proposal for thesis units (maximum of 10 units per semester). A minimum of 40 thesis units should be completed to submit the thesis for examination.

Year three through finishing the PhD program (fifth semester and beyond)

Year three marks the commencement of the fifth semester. Students are expected to present their progress at least twice each semester to their supervisors and their DAC. They are also expected to submit progress reports to their respective DAC members at least once per semester. Students are encouraged to present their research at seminars or conferences organized by BITS Pilani, OHT, and elsewhere.

• Field Work: Students are encouraged to undertake field visits depending on their thesis topic. They may conduct quantitative or qualitative data collection corresponding to their research interests.
• Dissertation Defence: The student will prepare their thesis in consultation with their team of supervisors and present/defend to their DAC. They will be required to write three research papers, which will form their dissertation, and publish in peer-reviewed journals to graduate.

To complete the PhD program, students must submit their thesis report within five years of starting their research. Students may be allowed to seek an extension from the doctoral counseling committee through their doctoral research committee (DRC), based on project requirements and individual circumstances. 

First cohort

Application Process

Applications to the PhD program are invited from candidates with a master’s degree in any basic science or liberal arts discipline. We also accept applications from candidates with a bachelor’s degree in medical, dental, veterinary, pharmaceutical sciences, alternative health sciences, and engineering. Applicants from other fields are also encouraged to apply.

The minimum eligibility requirements for admission are as follows:

• ME / MTech / MPharm / MBA / MPhil: minimum of 60 percent aggregate
• MSc / BE / BPharm or an equivalent degree: minimum of 60 percent aggregate
• MA: minimum of 55 percent aggregate
• MBBS / BDS / BVSc / MD / MDS / MVSc / BAMS / BHMS / BUMS / allied

Applicants should submit the following:

A statement of research purpose (maximum two pages), indicating the candidate’s academic background, broad research interests, career goals, and how a PhD in Data Sciences for Global Health from BITS Pilani–OHT will advance their career goals.

Two letters of recommendation

The shortlisted applicants will be interviewed about their knowledge of global health, data sciences, and research interests. OHT will participate in the interviewing panel. There will be no written exam, but grades from previous written exams will be considered.

1. When does the new application cycle start?

New application cycle opens in September or October of each year.

2. Who is eligible for this program?

We welcome applications from all professional, geographic, cultural, and socioeconomic backgrounds.  

Minimum eligibility criteria:  

• ME/MTech/ MPharm/ MBA/MPhil/ MSc/ BE/ BPharm or an equivalent degree: minimum of 60 percent aggregate  
• MBBS/ BDS/ BVSc/ MD/ MDS/ MVSc/ BAMS/ BHMS/ BUMS/ allied: minimum of 55 percent aggregate.

3. My previous degree is from outside of India. What documents will I need to submit for registration if I am accepted?

International students will be required to provide an appropriate grade conversion to the Indian grading system. Please write to the OHT team at  [email protected]  or the BITS Pilani team at  [email protected]  for more information.

4. Will the selection process include an interview?

Shortlisted applicants will be interviewed about their knowledge of global health, data sciences, and research interests. Written exams will not be conducted, but grades from previous degrees (transcripts), a statement of purpose, and letters of recommendation will be required.

5. When should I receive a decision on my application?

Applicants will receive a decision on their application within eight weeks following the close of the application window.

6. Is this an on-campus full-time program or a hybrid program?

This PhD is offered only as an on-campus full-time program.  

7.  Can the primary focus of the thesis be focused on policy?  

It is critical to have a data science focused thesis, but we encourage thesis topics that are policy oriented.  

8.How is the program structured?  

Please refer to the ‘Course Structure’ section. The courses aim to build research skills in data sciences for global health.  

9. How is PhD candidates’ time allocated between the OHT and BITS Pilani campuses?  

Time spent at each campus can vary depending on research.  

10. Will there be any funding provided for research?  

We offer institutional fellowships/stipends for the duration of the program to cover living costs. Internal research funds are available and will be competitively awarded. Students can apply for external funding to support their research. Finally, students have the opportunity to work as part of current research projects at OHT and BITS Pilani. 

11. Is it possible for students to change their supervisor after being assigned one at the time of their acceptance to the program?

Students will need to file a formal request for a change of supervisor and pending approval from an internal committee at BITS, they can change their assigned supervisor.  

12.  Is wet lab research within the scope of this PhD?  

This program focuses on data science, but additional wet lab work can be conducted if required by the thesis topic and supported by research funds.

13. What are the locations of the BITS Pilani campuses that offer courses applicable to this program?

The campuses with courses applicable to the program include:  

BITS Pilani, Hyderabad Campus  

Jawaharnagar, Shamirpet Mandal Medchal-Malkajgiri District Hyderabad 500078 Telangana   Google Maps  

BITS Pilani, Goa Campus  

NH 17B, Bypass, Road Zuarinagar, Sancoale Goa 403726   Google Maps  

BITS Pilani, Pilani Campus  

Vidya Vihar Pilani Rajasthan 333031   Google Maps  

14.  What is the fee structure for this program?  

The following are the details of the fees in INR payable by all students admitted to the PhD Programmes in the academic year 2024-2025 at BITS- Pilani, Pilani Campus/ KK Birla Goa Campus/ Hyderabad Campus.

* Payable at the time of admission; # payable at the time of thesis submission.

15. What are the potential sources of financial assistance given to PhD students?

Potential sources of financial assistance are:  

• self-funded fellowships, such as  UGC/CSIR NET JRF ,  DBT JRF/SRF ,  ICMR JRF/SRF ,  DST  Inspire;  
• the BITS institutional fellowship (stipend).  

 16. What is the expected stipend?

Stipends can range from INR 34,000/month during the first year to INR 37,000, provided the duration of the program.

17. If a student has a self-funded fellowship such as JRF/SRF, will they still be expected to conduct teaching duties on the BITS campus?

PhD candidates will still be expected to conduct teaching duties in the case that they have a self-funded fellowship.

18. If a student has a self-funded fellowship such as JRF/SRF, will they be able to receive the BITS Pilani fellowship?

Students will receive either a self-funded fellowship (JRF/SRF) or a BITS institutional fellowship (stipend).

19. As a teaching assistant (TA), how many hours of classes is a student expected to assist in a week?

On an average, TAs will work eight hours per week.  

20. Does BITS Pilani or OHT provide accommodation?

Accommodation will be provided for doctoral students at special rates.  

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UCLA Graduate Programs

Graduate Program: Data Science in Health

UCLA's Graduate Program in Data Science in Health offers the following degree(s):

Master of Data Science in Health (M.D.S.H.)

With questions not answered here or on the program’s site (above), please contact the program directly.

Data Science in Health Graduate Program at UCLA Suite 51-254 CHS Box 177220 Los Angeles, CA 90095-1772

Visit the Biostatistics Department’s faculty roster

COURSE DESCRIPTIONS

Visit the registrar's site for the Biostatistics Department’s course descriptions

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(310) 825-5250

[email protected]

MAJOR CODE: DATA SCIENCE IN HEALTH

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• EPSRC Centre for Doctoral Training in Health Data Science

EPSRC Centre for Doctoral Training in Healthcare Data Science

Course structure

Research & ethics, student experiences, follow us @hdscdt.

Insights derived from the analysis of large, complex data sets will make significant contributions to the prevention and treatment of disease. The aim of this doctoral training  programme  in Healthcare Data Science is to offer systematic training in statistics, machine learning, and data management. Core to the entire  programme  is to combine this technical training with a foundation in ethics.  

Ethics plays a central role in health data science, and our approach to doctoral training reflects this. Ethics and research responsibility is a vertical theme running through the four years of the  programme . Each of the first two terms begins with a week of training in ethics, responsible research and innovation, and collaborative working. In the first term, this training addresses ethical issues in data science and big data in general. In the second, it focuses upon specific ethics and governance issues in health data science and healthcare delivery.  

This EPSRC Centre for Doctoral Training in Healthcare Data Science  is located in  the Big Data Institute/Oxford Population Health Building at the University of Oxford.

ENTRY REQUIREMENTS

A data science subject degree including Mathematics, Statistics, Engineering Science, Computer Science or a related field with substantial mathematical background. Applicants are recommended to have completed an MSc in one of the above subjects.  

How to apply

We're delighted that this programme has been renewed as the EPSRC CDT in Healthcare Data Science for another five cohorts starting from October 2024. Please see the admissions page on the University website for information about entry this autumn.

This course is taking part in a continuing pilot programme to improve the assessment procedure for graduate applications, to ensure that all candidates are evaluated fairly. For this course, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes where it has been provided. Please carefully read the instructions concerning submission of your CV/résumé, statement of purpose, transcript and letters of support from referees in the  How to apply  section of this page as well as the  full details about this pilot .

It is important that you follow these new steps for your application to be considered.  Please use the standardised CV template provided and do not upload your own personalised version as these will not be reviewed by the Directorate.

Please ignore the section that states referees should anonymise their references, this applied to other courses on the pilot scheme but not ours.

We suggest considering Reuben College  or Kellogg College as the CDT has forged partnerships with these colleges. You are of course free to select any college on your application form but the CDT encourages you to consider one of these two listed colleges.

RESEARCH ENVIRONMENT

The Centre is hosted within the Big Data Institute (BDI)/Oxford Population Health (OxPop) Building, a purpose- built building  at the heart of the University of Oxford's biomedical campus. The Big Data Institute is an analytical hub for multi-disciplinary working at Oxford, connecting world-leading expertise in statistics, computer science, and engineering to data-driven research in clinical medicine and population health. It is also home to the  Ethox  Centre , a world-leading  centre  for clinical and research ethics, and the   Oxford Centre for Ethics and Humanities . 

The building hosts the clinical informatics and big data activity of the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC), a substantial  programme  of translational research, delivered by the University in partnership with Oxford University Hospitals (OUH) NHS Foundation Trust .  CDT students will have the opportunity to contribute to the work at the BRC, to access the expertise of the team, and to become involved in multi- centre  research collaborations.  

The BDI/OxPop Building is also home to UK Biobank , a major national and international resource for health research. The Biobank team are leading the development of tools for the acquisition, processing, analysis, and re-use of data from clinical and online assessments, imaging, sensors, genotyping, and national datasets (including hospital episodes, death, and primary care) for a cohort of 500,000 participants. CDT students will have the opportunity to access the expertise of the team, and to become involved in Biobank-based research.  

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Public Health Data Science

The MS in Biostatistics Public Health Data Science Track (MS/PHDS) is designed for students interested in careers as biostatisticians applying statistical methods in health-related research settings. The MS/PHDS Track provides core training in biostatistical theory, methods, and applications, but adds a distinct emphasis on modern approaches to statistical learning, reproducible and transparent code, and data management. It is an appropriate program for students who intend to conclude their studies with the MS degree as well as those who want to pursue a PhD in biostatistics

All MS/PHDS candidates begin their studies in the fall semester. The length of the MS/PHDS program varies with the background, training, and experience of the candidate, but the usual period needed to complete the 36 credit MS/PHDS degree is two years (four semesters). In addition to fulfilling their course work, all MS/PHDS students also complete a one-term practicum and capstone experience.

Competencies

Through a curriculum of 36 credit hours of course work, a practicum, and the capstone experience, the MS/PHDS track provides students with the skills necessary for a career as a public health data scientist and a rigorous grounding in traditional biostatistics.

In addition to achieving the MS in Biostatistics core competencies, students in the PHDS Track gain the following specific competencies in the areas of public health and collaborative research, the foundations of applied data science, teaching biostatistics and biostatistical research. Upon satisfactory completion of the MS/PHDS, graduates will be able to:

Public Health and Collaborative Research

• Formulate and prepare a written statistical plan for analysis of public health research data that clearly reflects the research hypotheses of the proposal in a manner that resonates with both co-investigators and peer reviewers;
• Prepare written summaries of quantitative analyses for journal publication, presentations at scientific meetings, grant applications, and review by regulatory agencies;

Foundations of Applied Data Science

• Develop expertise in one or more statistical software and database management packages (often R and SQL, among others) routinely used by data science professionals;
• Implement a reproducible workflow for data analysis projects, including robust project organization, transparent data management, and reproducible analysis results;
• Develop and execute analysis strategies that use traditional statistical tools or modern approaches to statistical learning, depending on the nature of the scientific questions of interest;
• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning;

Teaching Biostatistics

• Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, statistical learning, and data analytic techniques to public health students enrolled in introductory level graduate public health courses; and

Biostatistical Research

• Apply probabilistic, statistical, and data scientific reasoning to structure thinking and solve a wide range of problems in public health.

Course Requirements

MS/PHDS graduates are expected to master the mathematical and biostatistical concepts and techniques presented in the curriculum’s required courses. Each student's program is designed on an individual basis in consultation with a faculty advisor taking into consideration the student's prior educational experience.

Students who have mastered an academic area through previous training may have the corresponding course requirement waived. Some students, such as those with undergraduate majors in statistics or mathematics, may apply to have several courses waived. Students wishing to waive one or more courses must request approval in writing from their advisors and the Director of Academic Programs. These students must still complete a minimum of 36 points to earn the MS/PHDS degree.

Required Courses

Below is the required course work. Students consult their faculty advisors before registering for classes to plan their programs based on their individual background, goals, and the appropriate sequencing of courses. Waiver of any required courses (with prior written approval of their faculty advisor and the Director of Academic Programs) enables students to take other, higher level classes.

*Students who have strong math background and/or have taken basic machine learning methods, can substitute the P8106 Data Science II with P9120 Topics in Statistical Learning and Data Mining I. 

Students choose four or more courses from the list below or from alternatives approved by their academic advisors.

Sample Timeline

Below is a sample timeline for MS/PHDS candidates. Note that course schedules change from year to year, so that class days/times in future years will differ from the sample schedule below; you must check the current course schedule for each year on the course directory page .

Practicum Requirement

One term of practical experience is required of all students, providing educational opportunities that are different from and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the practicum experience. Students will be required to make a poster presentation at the department’s Annual Practicum Poster Symposium which is held in early May.

Capstone Experience

A formal, culminating experience for the MS degree is required for graduation. The capstone consulting seminar is designed to enable students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/collaborator, which will comprise the major portion of their future professional practice.  

As part of the seminar, students are required to attend several sessions of the Biostatistics Consulting Service (BCS). The Consultation Service offers advice on data analysis and appropriate methods of data presentation for publications, and provides design recommendations for public health and clinical research, including preparation of grant proposals. Biostatistics faculty and research staff members conduct all consultation sessions with students observing, modeling, and participating in the consultations.

In the capstone seminar, students present their experience and the statistical issues that emerged in their consultations, developing statistical report writing and presentation skills essential to their professional practice in biomedical and public health research projects.

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PhD in Health Data Science

The PhD Program in Health Data Science at FMUP offers advanced education focused on a markedly multidisciplinary vision in three areas of specialization: Health Informatics, Intelligent Analysis of Health Data; Health Interventions, Policies, and Services.

The Program is aimed at students with a relevant academic or scientific curriculum, with academic education or professional experience in areas such as Medicine, Health Sciences and Technology, Computer Science, Computer Engineering, Mathematics, Statistics, Psychology, Economics, or Management, among others.

With 240 ECTS, the objectives of the cycle of studies include the definition, development, interpretation, and synthesis of health research results, the application of methods for statistical analysis, the integration of health data results into daily practice, and the transposition of the knowledge obtained into health decision making, considering ethical, legal and health data quality issues.

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• Health Intelligent Data Analysis (240 ECTS credits)
• Health Interventions, Policy and Services (240 ECTS credits)
• Health Informatics (60 ECTS credits)
• Health Interventions, Policy and Services (60 ECTS credits)
• Health Intelligent Data Analysis (60 ECTS credits)

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Statistics Data Science: Ph.D.

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Our Ph.D. in Statistics Data Science program offers you the opportunity to hone your skills in mathematical reasoning, statistical modeling, computation, and methodology development.

Through this new doctoral program, you will gain a thorough understanding of probability and statistics as well as machine learning methods. You’ll apply statistical methods and theory to real-world data challenges in an interdisciplinary manner. This program will expose you to cutting-edge research and developments in statistics, machine learning, artificial intelligence and data sense, preparing you for statistics and data science careers in academia, the public sector and industry.

Jump to:   Admission & Degree Requirements   |  Application Deadlines   |  Research Areas   |  Faculty  

Admission & Degree Requirements

Admission to this program is highly competitive and selective. We require you to submit the following with your application. 

All transcripts from undergraduate and graduate (if applicable) institutions. 

Three letters of recommendation.

Personal statement: Include research interests;do not exceed three pages.

GRE General test score (required but can be waived for students currently enrolled in or have already earned the MS in Statistics degree in UD)

GRE subject test in Mathematics or other STEM fields (optional).

Language scores (for international students whose native language is not English, and who have not received a degree at a U.S. college or university). A score of 100 or higher on the Test of English as a Foreign Language (TOEFL), or equivalently 7.5 or higher on the International English Language Testing System (IELTS). 

A department graduate committee will decide who is admitted to the program in compliance with University policies and procedures. The committee reserves the right to interview the applicants.

Students with an MS degree in Statistics or related fields are eligible for a 4-year accelerated track with a reduced course load. Eligibility is determined by the admission committee.

Degree Requirements

You must have, or expect to have a bachelor’s degree or higher in statistics, mathematics or a related field from an accredited college of university, by the date of admission.

Ready to Apply?

Apply now >, view course and exam requirements >, email the program director >.

* Disclaimer: The customized GPT is an experimental tool designed to provide real-time answers based on the official curriculum and commonly asked questions. GPT-generated answers may not always be accurate. Please verify all information through the official University of Delaware website.

Application deadlines

Regular admission is for each fall semester. Applicants must submit their application via the online link no later than February 1 .

Research Areas

The Statistics faculty within the department engage in a broad range of research topics. Our expertise spans classical statistical problems, such as hypothesis testing, high-dimensional data analysis, dimension reduction, time-series analysis, and nonparametric statistics, as well as contemporary topics, including network modeling, graph learning, neural networks, computational statistics, and optimization. Additionally, our faculty are actively involved in data-driven research applications across diverse fields, such as large language models, image data analysis, financial forecasting, health sciences, biology, and animal science.

The program also offers students the flexibility to pursue research in collaboration with our affiliated faculty or any other University of Delaware faculty whose work is closely aligned with statistics and data science. This interdisciplinary approach provides a unique opportunity for students to tailor their research experience to their academic and professional interests.

Core Faculty  

Dr. Shanshan Ding

Dr. Wei Qian

Dr. Jing Qiu

Dr. Cencheng Shen

Dr. Peng Zhao

Affiliated faculty

Dr. Austin Brockmeier

Dr. Rahmat Beheshiti

Dr. Yin Bao

Dr. Jeff Buler

Dr. Kyle Davis

Dr. Vu Dinh

Dr. David Hong

Dr. Mokshay Madiman

Dr. Xi Peng

Dr. Guangmo (Amo) Tong

Dr. Xu Yuan

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