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76 PhD jobs in Machine Learning

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Search results (76)

PhD Candidate for Computational Approaches for Studying Animal Behavior, Personality, and Emotions

About our Campus: Rehovot Campus (Rehovot) and Mt. Scopus Campus (Jerusalem). The position will be based across two campuses: The Robert H. Smith Faculty of Agriculture, Food and Environment on The Hebrew University’s beautiful Rehovot Campus. Reh...

PhD position on Practical AI Planning with Ontologies 100%

The Artificial Intelligence (https://ai.dmi.unibas.ch/) research group at the Department of Mathematics and Computer Science, University of Basel, is seeking a PhD candidate for a collaborative pro...

Doctoral Student in Computational Chemistry

Doctoral Student in Computational ChemistryProf. Dr. Sereina Riniker's group for Computational Chemistry at the Institute of Molecular Physical Science is interested in the development of methodolo...

PhD and Post-doc positions in the field of Optimization and Deep Learning Theory (Prof. A. Lucchi)

The Optimization of Machine Learning Systems Group (Prof. A. Lucchi) at the Department of Mathematics and Computer Science at the University of Basel is looking for several PhD students and post-do...

PhD in construction site monitoring through Computer Vision

There is a vacancy for a PhD researcher in the Geomatics research group of the Department of Civil Engineering of KU Leuven. The candidate will be active in projects that aim at using computer visi...

PhD Positions in Cancer Research

Are you looking for excellent research opportunities for your PhD studies at the forefront of cancer research? The German Cancer Research Center (DKFZ) in Heidelberg invites international students holding a Master’s degree in (molecular) biology, ...

Doctoral Researcher (PREIN I-DEEP Doctoral education pilot), 2 positions

Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high-impact research and education. Our universities community has its competitive edges in technology, health and s...

PhD Researcher in Learning-based control algorithms for avatar animation in dyadic interactions

Last application date Dec 31, 2024 00:00Department TW05 - Department of Information TechnologyContract Limited durationDegree European master's degree in computer science, Artificial Intelligence, or equivalentOccupancy rate 100%Vacancy type Resea...

Three 2-year Postdoctoral Fellowships at the Zukunftskolleg

(Fulltime, E 13 TV-L)Reference No: 2024/114. The preferred start date is April 1st, 2025. Conditionally on the submission of an external grant, the position can be extended for an additional year. In principle, the position can be divided into two...

PhD on Stochastic modelling and reliability assessment

Disruptive innovations are needed in managing and operating distribution grids. Are you our next PhD researchers in exploring disruptive innovations in managing and operating distribution grids?Position PhD-studentIrène Curie Fellowship NoDepartme...

PhD student Causal Machine Learning

Last application date Sep 01, 2024 00:00Department WE02 - Department of Applied Mathematics, Computer Science and StatisticsContract Limited durationDegree Master of Statistics, or Master of Mathematics, Physics, Computer Science, Engineering (eac...

Ph.D. Student in Machine Learning for Biosignal Processing

The University of Applied Sciences and Arts of Southern Switzerland (SUPSI) has opened a full time (100%) position for a Ph.D. Student in student in Machine Learning for Biosignal Processing at the Department of Innovative Technologies (DTI) locat...

PhD Student - Department of Information Technology

Last application date Aug 31, 2024 00:00Department TW05 - Department of Information TechnologyContract Limited durationDegree Master in computer science, Artificial Intelligence, or equivalent, with excellent ('honors'-level) gradesOccupancy rate ...

Doctoral Researchers: 8 Fully Funded Positions in Data Science & Health

The German Cancer Research Center, Karlsruhe Institute of Technology and University Heidelberg are seekingDoctoral Researchers:8 Fully Funded Positions in Data Science & HealthDo you want to bring artificial intelligence (AI) and data science to t...

STEP UP! Fellowship programme for early career scientists

As part of the STEP UP! Fellowship programme run on a 2-year cycle, the Deutscher Wetterdienst awards fellowships to early career scientists to work on a research project during a guest stay at the European Centre for Medium-range Weather Forecast...

PhD candidates - Spontaneous Applications for Next Generation Radar Sensing Networks

(Valid from 22/01/2024 to 31/12/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Interdisciplinary Centre of Security, Reliability and Trust Job Number: UOL05451 Contract Type: Fixed Term Contract Duration 36 Month Schedule ...

PhD student computer science, specialisation human-computer interaction

BackgroundThe research center Expertise Center for Digital Media (EDM, www.uhasselt.be/edm) has been conducting (mainly) software-oriented research in computer science for more than 25 years. The E...

Upcoming PhD Positions at the International Max Planck Research School for Molecules of Life in Autumn 2024

The next application round will start in September 2024. Stay tuned!The International Max Planck Research School for Molecules of Life (IMPRS-ML) currently has an open call for fully funded PhD student positions in the areas of biochemistry, struc...

PhD position in Large Language Models for education

We have an open Phd position that is part of a large interdisciplinary research project. Collaborating research groups include the Leuven Engineering and Science Education Center (supervisor Tinne ...

PhD positions in Sustainable Data-centric Control

Description:Multiple PhD positions are available in the Mathematical Engineering Department at the University of Louvain, Belgium in the context of a newly-started research project. The goal of the project is to develop learning and control tools ...

Modeling dendrite growth in Li-ion battery materials

This is a joint PhD position at KU Leuven (Belgium) and Silesian University of Technology (Poland). As a PhD student on this project, you will first work for 2 years at KU Leuven (multi-physics mic...

PhD in geomatics: damage detection in historic masonry using computer vision and AI

There is a vacancy for a PhD researcher in the Geomatics research group of the Department of Civil Engineering of KU Leuven. The candidate will be active in the CHAI project (Cracking Built Heritag...

Precision Insect Farming: Development of a novel sensor platform to monitor stress, behavior and welfare of insect larvae in relation to the rearing conditions (PhD)

This open PhD position is embedded in a ground-breaking collaboration between the Livestock Technology group (prof. Ben Aernouts) and the research group for Insect Production and Processing (prof. ...

Novel machine learning and artificial intelligence techniques for food quality monitoring (PhD)

Investing in sustainable food production and food security is more relevant than ever before. Livestock farming is an important link in this process as animals have the ability to convert low-value...

PhD Position in Machine Learning for Green Credit Scores

Job descriptionThe goal is to resolve how financial settings can adapt practices or propose solutions for designing green credit scoring models. This will facilitate improved access to investment a...

PhD Position on designing a recommender system for sustainable investments

Job descriptionDuring the project, you will closely collaborate with industry and a doctoral training network spread throughout Europe, including extended research stays abroad.The successful appli...

PhD Student in Generative Modelling of Conversational Dynamics

Project descriptionThird-cycle subject: Computer ScienceThis project aims to create generative models of spoken conversation that enable speaking machines to adapt their conversation style over tim...

PhD Student in Physics

The University of Gothenburg tackles society’s challenges with diverse knowledge. 56 000 students and 6 600 employees make the university a large and inspiring place to work and study. Strong research and attractive study programmes attract resear...

Doctoral student in Underwater Robotics and Machine Learning

Project descriptionThird-cycle subject:  Computer Science The project involves the use of multiple autonomous underwater vehicles to inspect and monitor an area quickly.  The use of machine learnin...

PhD Position in Machine Learning and Computer Vision

Positions: We have an opening for a PhD position in the areas of machine learning and computer vision. The position is supported financially by a SNSF project, whose aim is to build con- trollable world models with deep learning methods. World mod...

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Machine Learning Career: Pros and Cons of Having a PhD

• September 25, 2021 at 4:30 pm November 28, 2022 at 12:02 pm

It is often said that data science jobs are for seasoned professionals, and many job ads still show a preference for a profile with a PhD, with years of experience. Yet, many corporate employers have been disillusioned about the value that a PhD brings to the company. Likewise, many professionals, especially among those who just completed a PhD and were offered their first job, find the work sometimes unrewarding.

A PhD may command a slightly higher salary initially, and may be required for a position in a research lab (whether private or government-operated). But for many positions, it may not bring an advantage. Corporate work can be mundane and fast-paced, and the search for perfect algorithms is discouraged, as it hurts ROI. In many companies, a solution close to 80% of perfection is good enough, and requires far less time than reaching 99% perfection, especially since the machine learning models employed are just an approximation of the reality. People with a PhD are not well prepared for that.

Here are some of the negative aspects.

• Even if you pay someone to write your PhD thesis (such services exist), you may spend several years of your life working on your PhD, possibly in a stressful environment, with low pay, delaying buying a home, or getting married. Meanwhile, you see your non-PhD friends ahead of you in their personal life. If you married when working on your PhD, this could eliminate some of these problems.
• Some recruiters may say that you are over-qualified, that your experience is not really relevant to the job you are applying for (or too specialized), and that adapting to a fast-paced corporate environment might be challenging.
• If you land a job in the corporate world, you might find it menial or boring. You could be disappointed that the research you did during your PhD years is a thing of the past, not leading to anything else. This is especially true if your hope was to get a tenured position in the academia, but can’t get one despite your very strong credentials, due to the fierce competition. It can bring long-lasting regrets and nostalgia.
• You may be lacking some coding skills (SQL in particular), which put you at a disadvantage against a candidate with an applied master. Of course, it is always possible and desirable to gain these skills on your own (or via data camps) when working on your PhD.
• Your salary might not be higher than that of a younger candidate with a master degree and the right experience. Your cumulative wealth over your lifetime may be lower.
• Some employers (Google, Facebook, Microsoft, Wall Street,  or defense-related companies) routinely hire PhD’s to work on truly exciting projects. Some only hire from top universities and if your PhD was not from an ivy-league,  you will be by-passed. That said, there are plenty of companies that will hire non ivy-league candidates, and I think that’s a smart move. After all, I earned my PhD in some unknown university, and eventually succeeded in the corporate world.

For some, the pros outweigh the cons by a long shot. This was my case. I provide a few examples below.

• If your PhD was very applied in a hot field (in my case in 1993, processing digital satellite images for pattern detection), you learned how to code, played with a lot of messy data, and even got part-time job in the corporate world, related to your thesis when working on it, then you are up to a good start. In my case, solid funding for the research, and even data sets, came from governmental agencies (EU and others) and private companies (Total, for instance) trying to solve real problems. This adds credibility to your PhD experience. On the downside, my mentor was not a great scholar, but a good salesman able to attract many well paid contracts.
• If you earned your PhD abroad like I did, it is quite possible that you were paid better than your peers in US. In my case, my salary, as a teaching assistant, was similar to that of a high school teacher. And conference attendance (worldwide) was paid by the university or by the agencies that invited me as a speaker. Coming from abroad is sometimes perceived as an advantage, due to showing cultural adaptation, and in most cases, being multilingual and able to easily relocate in various locations if corporate needs ask for it.
• You can still continue to do your research, decades after leaving academia. I still write papers and books to this day. The level is even higher than during my PhD years, but the style and audience is very different, as I try to present advanced results, written in simple English, to a much larger audience. I find this more rewarding than publishing in scientific journals, read by very few, and obfuscated in jargon.
• There are great positions in many research labs, private or government, available only to PhD applicants. The salary can be very competitive.
• VC funding is usually contingent to having a well-known PhD scientist on staff, for startup companies. So if you create your own startup, or work for one, a PhD is definitely an advantage. Even when I started my own, self-funded publishing / media company (acquired by Tech Target in 2020, and focusing on machine learning), my wife keeps reminding me that I would have had considerably less success without my education, even though you don’t legally need any degree or license to operate this kind of business.

Conclusions

Having a PhD can definitely offer a strong advantage. It depends on the subject of your thesis, where you earned your PhD, and if you worked on real-life problems relevant to the business world. More theoretical PhD’s can still find attractive jobs in various research labs, private or government. The experience may be more rewarding, and probably less political, than a tenured position in academia. It goes both ways: it is not unusual for someone with a pure corporate / business background, to make a late career move to academia, sometimes in a business-related department. Or combining both: academia and corporate positions at the same time.

I wrote an article in 2018, about how to improve PhD programs to allow for an easy  transition to the business world. I called it a doctorship program, and you can read about it  here . I will conclude by saying that another PhD scientist, who earned his PhD in the same unknown math department as me at the same time (in Belgium), ended up becoming an executive at Yahoo, after a short stint (post-doc) at the MIT, working on transportation problems. His name is Didier Burton. Another one (Michel Bierlaire), same year, same math department, also with a short post-doc stint at MIT (mine was at Cambridge University), never got a corporate job, but he is now an happy full professor at EPFL. Also, a Data Science Central intern (reporting to me), originally from Cuba and with very strong academic credentials (PhD, Columbia University, EPFL) got his first corporate job after his internship with us (I strongly recommended him). Despite a mixed academic background in physics and biology, he is now chief data scientist of a private company. His name is Livan Alonso.

About the Author

Vincent Granville is a pioneering data scientist and machine learning expert, founder of  MLTechniques.com  and co-founder of  Data Science Central  (acquired by  TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).

Vincent published in  Journal of Number Theory ,  Journal of the Royal Statistical Society  (Series B), and  IEEE Transactions on Pattern Analysis and Machine Intelligence . He is also the author of multiple books, available  here . He lives  in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.

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Best Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salaries

If you want to take your career in machine learning to the next level, you might be considering enrolling in one of the best PhDs in machine learning. However, it can be hard to figure out which program is right for you, how to fulfill all the requirements, or how to secure the right funding opportunities so you can continue your education in this field.

This comprehensive guide will look at the best options for a machine learning PhD, both in-person and online. We’ll also discuss the best machine learning jobs and how to get them with this type of degree, as well as the average PhD in machine learning salary you can earn upon graduation.

Find your bootcamp match

What is a phd in machine learning.

A PhD in machine learning is a research-intensive degree program that helps students further their education in machine learning. A machine learning PhD is a doctorate degree that involves coursework, qualifying exams, and oral examinations. Professors and members of faculty also work closely with students to help them develop a strong dissertation throughout their degree program.

Students interested in pursuing a PhD in machine learning should have already completed a bachelor’s degree in a relevant field. They also need to have completed a master’s degree , or commit to completing it along the way.

How to Get Into a Machine Learning PhD Program: Admission Requirements

The admission requirements to get into a machine learning PhD program typically include filling out an application form and submitting an application fee, academic transcripts from your undergraduate degree, two to three recommendation letters, a statement of purpose, GRE scores, a resume, writing sample, and English proficiency test scores for international students.

Each school’s website will have a detailed breakdown of all the requirements needed for the application process. Some schools may require you to pay an application fee, have a minimum GPA score, and take the Graduate Record Examination (GRE), although most schools have waived this requirement until 2023.

You will need two or three recommendation letters for your PhD application. The recommendation letter should be from faculty members and colleagues familiar with your work. Part of the application process is a statement of purpose, which is an essay that should tell the admission committee why you want to pursue a PhD in Machine Learning.

PhD in Machine learning Admission Requirements

• Application form
• Application fee
• College transcripts
• Minimum GPA of 3.0 (varies)
• Two to three recommendation letters
• Statement of purpose
• Writing sample
• English proficiency test (only for non-native English speakers)

Machine Learning PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Machine Learning?

It is hard to get into a PhD program in machine learning. Prestigious schools are usually very selective and have a low admission rate ranging between four and 30 percent. For example, Harvard University has an admission rate of  four percent, so make sure you prepare a strong application and have a high GPA if you want to get into Harvard or another highly-reputable university.

However, not all PhD programs are extremely selective. For instance, institutions in the University of California system have higher acceptance rates, such as 34.4 percent. To improve your chances of acceptance, you can ask a friend or mentor to look over your PhD application. You should also apply to more than one program.

How to Get Into the Best Universities

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Best PhDs in Machine Learning: In Brief

Best Universities for Machine Learning PhDs: Where to Get a PhD in Machine Learning

The best universities for machine learning PhDs include Carnegie Mellon University, Georgia Tech, and University of Washington. These schools can help you earn your machine learning PhD. If you’re wondering where you can get a PhD in machine learning, the list below discusses 10 excellent programs, along with their essential details.

Carnegie Mellon University was founded in 1900. It is known for its high-quality graduate programs in engineering, artificial intelligence (AI), and computer science. There are about 29 graduate degree programs offered at Carnegie Mellon University’s graduate school. Students and faculty conduct open and restricted research in four main areas, including AI, learning sciences, robotics, and neuroscience.

PhD in Machine Learning

The PhD in Machine Learning at Carnegie Mellon University requires students to take six core courses and one elective course. This research-focused degree program requires students to present and defend a thesis by the end of the program.

During this program, students need to work as teaching assistants for two semesters and will complete a presentation to show adequate presentation skills to the Speaking Skills committee. Common courses for this program include an introduction to machine learning, intermediate statistics, and regression analysis.

PhD in Machine Learning Overview

• Program Length: 5 years
• Acceptance Rate: 17%
• Tuition and Fees: $645/unit
• PhD Funding Opportunities: Graduate assistantships, scholarships, and grants

PhD in Machine Learning Admission Requirements

• GRE (recommended)
• TOEFL (for international applicants)
• Recommendation letters
• High level of knowledge in computer science and math

Georgia Institute of Technology is a reputable university founded in 1885. It is known for its excellent STEM majors, of which 86 percent of students are enrolled. It offers many graduate degree programs to its 25,011 graduate students, but the most well-known programs are in electrical and computer engineering, computer science, and mechanical engineering.

The PhD in Machine Learning at Georgia Institute of Technology will teach you excellent machine learning techniques through machine learning courses. Students need to complete four core courses, five elective courses, responsible conduct of research course, and three doctoral minors.

Typical courses for this PhD program include machine learning theory and methods, advanced theory, and computing and optimization. This program consists of many research hours and requires PhD students to complete the defense of a dissertation. Students also need to complete a qualifying exam.

• Program Length: 4 years
• Acceptance Rate: 21%
• Tuition and Fees: $586/credit (in state); $1,215/unit (out of state)
• PhD Funding Opportunities: Federal loans, private loans, federal work-study program
• Minimum GPA of 3.0
• Three letters of recommendation
• IELTS minimum score of 7.5 or higher for non-native speakers
• TOEFL minimum score of 100 or higher for non-native speakers
• GRE (optional)

Harvard University is a highly reputable and well-known private research university founded in 1636. It currently has about 33,276 students enrolled in undergraduate degrees, graduate degrees, and certificate programs. Harvard University has 12 graduate schools and a fantastic faculty, of which members have received Nobel prizes in chemistry, medicine, physics, literature, peace, and economic sciences.

PhD in Computer Science

The machine learning PhD program at Harvard University teaches students about the interaction of computation with the world and computation fundamentals. Students will work with highly-rated faculty members conducting research in programming languages, machine learning, and artificial intelligence during this excellent program. As they move through their program, students will learn about connecting computer science to other fields while they interact with lawyers, scientists, and engineers.

PhD in Computer Science Overview

• Acceptance Rate: 4%
• Tuition and Fees: $50,928/year
• PhD Funding Opportunities: Grants, fellowships, traineeships, research assistantships, and teaching fellowships

PhD in Computer Science Admission Requirements

• Transcripts
• At least one recommendation letter
• Show English proficiency (for non-native English speakers)

Northwestern University was launched in 1851 and is one of the top research universities in the country. Its more than 50 research centers focus on topics like nanotechnology, neuroscience, biotechnology, and drug discovery.

Currently, Northwestern university has over 13,000 grad students enrolled in its 173 graduate degree and certificate programs. Northwestern University is known for its fantastic business, education, and materials engineering degree programs.

PhD in Computer Science and Learning Sciences

The machine learning PhD program at Northwestern University is research-driven and helps students understand and build a connection between research on computation and learning. Students can choose between many different areas of study, including machine learning and programming language design.

To complete this program, there should be apparent relevance in your research between computer science and the learning science in your field of study, such as machine learning. You must also complete a qualifying exam, research projects, and a PhD dissertation. Courses include Machine Learning, Foundations of Learning Science, and Artificial Intelligence Programming.

PhD in Computer Science and Learning Sciences Overview

• Program Length: 4-9 years
• Acceptance Rate: 7%
• Tuition and Fees: $18,689/quarter for programs with 8 or fewer quarters; $4,672/quarter for more than 8 quarters
• PhD Funding Opportunities: Assistantships, grants, and fellowships

PhD in Computer Science and Learning Sciences Admission Requirements

• Online application form
• Academic transcripts
• GRE scores (temporarily not required, but still recommended)
• TOEFL scores (for international applicants) 

Tulane University was launched in 1834 and is in the top two percent of research universities in the US. Tulane University conducts research in bio-innovation, health, energy, and the environment. It offers over 200 graduate degrees to over 5,000 grad students.

Students at Tulane University graduate school can pursue PhDs in computer science, environmental health studies, economics, and more. This University offers excellent funding opportunities such as fellowships and stipends.  

The PhD in Computer science at Tulane University is a research-intensive program. Students must conduct research in a specific depth area such as machine learning, artificial intelligence, or data science. Students who specialize in machine learning will research machine learning techniques, theory of applications, machine learning systems, and algorithms.

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Topics covered during this graduate program include algorithms, machine learning, and computer networks. Students also need to take three research courses. Students will do a qualifying oral exam during this program, complete a prospectus presentation, and a PhD dissertation in their preferred specialization, such as machine learning.

• Program Length: 4-7 years
• Acceptance Rate: 9.73%
• Tuition and Fees: $1,831/credit; $35,088/year with 9 credits per semester 
• PhD Funding Opportunities: Scholarships, fellowships, and stipends
• University transcripts
• Statement of Purpose
• GRE test scores
• TOEFL scores (for international applicants)

University of California Irvine is a public land-grant university established in the 1960s as part of the University of California system. It is a research-focused institution and boasts eight  Nobel Prize winners among its alumni. The graduate school offers over 100 graduate programs. This university offers many different PhDs, including bioengineering, machine learning and data science, and mechanical engineering.

The PhD in Computer Science at University of California Irvine helps students learn computer science fundamentals and essential machine learning skills. This program involves a research project. Students need to choose a research topic such as machine learning and artificial intelligence, scientific computing, or any other research topic listed on the website. 

Students need to complete at least 47 units during their program and maintain a 3.5 GPA. Courses for this degree include Machine Learning, Machine Learning and Data Mining, and Analysis of Algorithms. Before the end of the program, students will complete a candidacy exam, submit a dissertation plan, complete a final exam, and defend their dissertations. 

• Program Length: 6-7 years
• Acceptance Rate: 28.96%
• Tuition and Fees: $18,037/year (in state); $33,139/year (out of state)
• PhD Funding Opportunities: Fellowships, graduate employment, research assistantships, and training grants
• English proficiency test scores (for international applicants)

University of California San Diego traces its roots back to 1960 and had its first enrollments in 1964. It offers over 200 degree programs at the undergraduate and graduate levels. It is a research-focused institution that conducts research in a variety of fields, from robotics and climate to microbiomes.

PhD in Machine Learning and Data Science

The PhD in Machine Learning and Data Science program teaches students essential machine learning techniques to help them further or start their careers in machine learning and artificial intelligence . During this graduate program, students need to complete 36 credit hours. They will conduct an in-depth research project, a preliminary exam, and a qualifying exam.

At the end of the PhD, students need to submit and defend a doctoral thesis. They are allowed to consider the faculty and choose a research advisor that fits their research style and goals. The research advisor will support the student through their PhD from start to finish. Courses included in this degree are Linear Algebra & Application, Deep Learning & Applications, Machine Learning for Image Processing, and Statistical Learning.

PhD in Machine Learning and Data Science Overview

• Program Length: 6-8 years
• Acceptance Rate: 34.3%
• Tuition and Fees: $ 11,442/year 
• PhD Funding Opportunities: Fellowships

PhD in Machine Learning and Data Science Admission Requirements

• GRE test scores (recommended)
• English proficiency test (for international applicants)
• Three recommendation letters
• High school and college transcripts

University of Pennsylvania is a research-driven university based in Philadelphia. It opened its doors to students in 1751. It prides itself on research and encourages students to conduct research during their studies. This university has twelve graduate schools that offer graduate degrees and certificates. Some of the fields for PhD level studies include biochemistry, economics, and materials science and engineering.

PhD in Computer and Information Science

The PhD in Computer and Information Science at the University of Pennsylvania has a specialization called Machine Learning + X, allowing students to choose machine learning and one other specialization to focus on throughout their programs. For example, you could choose to do a Machine Learning + Computer Architecture specialization.

This degree requires specific courses, a preliminary exam, a teaching assistantship, a defense proposal, a defense of your dissertation, and a submission of your thesis. This PhD will help students gain new machine learning skills and experience in machine learning.

PhD in Computer and Information Science Overview

• Tuition and Fees: $19,919/year for the first eight semesters; $1,836 flat rate after the first eight semesters
• PhD Funding Opportunities: Fellowships, teacher assistantships, and research assistantships

PhD in Computer and Information Science Admission Requirements

• Personal statement
• Unofficial academic transcripts
• Three official recommendation letters
• GRE scores (optional until 2023, but still recommended)

This public research university was established in 1895 and is known for its high-quality doctoral research. University of Texas at Arlington has more than 174 graduate degrees and other graduate study options. New and current students can pursue a PhD in different fields like computer science, civil engineering, and mathematics. 

The PhD in Computer Science offered by University of Texas at Arlington allows students to choose a study track. There are eight options, but students interested in machine learning should choose the intelligent systems track, which covers machine learning methods, neural networks, parallel AI, and more.

Throughout this degree program, students will complete 18 hours of coursework and complete two comprehensive exams, one of which is a dissertation proposal. They will also submit a final dissertation defense before being awarded their PhD.

• Program Length: 4-5 years
• Acceptance Rate: Not stated
• Tuition and Fees: $11,044/year (in state); $23,486/year (out of state)
• PhD Funding Opportunities: Teacher’s assistantships, research assistantships, fellowships, grants, and scholarships
• College transcripts 

University of Washington is a highly reputable school located in Washington that started conducting classes in 1861. It is known for its high-quality research and boasts that seven of its researchers have won Nobel prizes in physics, physiology, and medicine.

New and current students at University of Washington can choose to continue their education with over 300 graduate degree programs offered at its three campuses. This university provides PhDs in physics, mathematics, and machine learning and big data.

PhD in Machine Learning and Big Data

The PhD in Machine Learning and Big Data program at University of Washington teaches students valuable machine learning methods and how to conduct data analysis of big data sets. It will help students build a strong foundation in machine learning and big data methodologies.

Students need to meet the coursework requirements, write a general examination, conduct research to write a dissertation, and meet the credit hour requirement of 90 credits. Courses included in this PhD are Foundational Machine Learning, Advanced Machine Learning, and Advanced Statistical Learning.

PhD in Machine Learning and Big Data Overview

• Program Length: Up to 10 years
• Acceptance Rate: 10.58%
• Tuition and Fees: $6,725/quarter (in state); $11,688/quarter (out of state)
• PhD Funding Opportunities: Fellowships, internships, and research assistantships

PhD in Machine Learning and Big Data Admission Requirements

• GRE scores (optional)
• Funding application

Can You Get a PhD in Machine Learning Online?

No, you cannot get a PhD in machine learning online. However, you can pursue an online PhD in computer science with a machine learning component such as an online machine learning course or specialization. Many fantastic online computer science PhDs will help you fine-tune your machine learning expertise.

Best Online PhD Programs in Machine Learning

How Long Does It Take to Get a PhD in Machine Learning?

It takes between four and 10 years to get a PhD in Machine learning. According to Statista, the average time to complete a doctorate degree is seven and a half years. A PhD takes this long to complete because it is research-intensive and involves several stages.

Students need to take required courses and complete coursework in the first two years of a PhD program. Once the coursework is complete, students will write an examination to ensure they have completed all the essential skills and expertise in machine learning.

In the final years of a PhD, students conduct research and write a dissertation which takes between two to five years to finish. Usually, the school will have information on their website regarding the maximum time students have to meet all the PhD requirements.

Is a PhD in Machine Learning Hard?

Yes, a PhD in Machine Learning is hard because it is research-driven. If you decide to pursue a PhD in machine learning, you need to ensure that you are motivated and determined to work hard because this program involves many hours of independent research and writing.

A PhD is also a lengthy degree program that takes a minimum of four years to complete. Don’t let the difficulty of a PhD in machine learning discourage you, though. If you are determined and enjoy researching and learning about machine learning, you will succeed.

How Much Does It Cost to Get a PhD in Machine Learning?

It costs $19,314 annually to get a PhD in Machine Learning , according to the figures from 2019 stated by the National Center for Education Statistics (NCES). The total tuition of your machine learning PhD depends on specific factors, including format, location, school, and specialization.

Colleges and universities are usually public or private institutions. Depending on what kind of school you attend, the tuition will differ. The average tuition for a PhD at a public institution is $12,171, while a PhD at a private institution costs $25,929. Search your school’s website or contact it directly to learn about the specific tuition costs of your PhD program.

How to Pay for a PhD in Machine Learning: PhD Funding Options

The funding options that students can use to pay for their PhD in machine learning include research assistantships, teaching assistantships, fellowships, internships, grants, and stipends. These funding options will lighten the financial burden of pursuing a PhD in machine learning.

Some schools offer teaching assistantships to students. You work a certain number of hours per week and receive a stipend or a tuition waiver or discount. A research assistantship is similar to a teaching assistantship, but they have different duties. According to Statista, research assistantships are the most common funding option for doctoral degrees .

Find out directly from your school if there are available paid internships, along with any other funding opportunities for PhD students in machine learning. Some schools award funding opportunities to students nominated by faculty members.

Best Online Master’s Degrees

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What Is the Difference Between a Machine Learning Master’s Degree and PhD?

The difference between a machine learning master’s degree and a PhD is that a PhD is research-intensive and focused, while a master’s degree is more focused on one’s career and may or may not include research for a master’s thesis.

A PhD is the highest degree level that a person can pursue, whereas a master’s degree is one level below. According to Statista, PhD degree holders make more than master’s degree graduates . Upon completing a master’s degree, students can earn an average salary of $92,272, while PhD graduates earn an average salary of $136,702.

Master’s vs PhD in Machine Learning Job Outlook

You can get a job as a computer information research scientist with a master’s degree, which comes with a job outlook of 22 percent . This is much faster than the average job outlook. With a PhD in machine learning, you can get any job in machine learning, but a job that explicitly requires a PhD is a university lecturer.

The job outlook for a machine learning lecturer is 12 percent , according to information cited by the US Bureau of Labor Statistics (BLS). This job outlook is much lower than that of a computer information research scientist. However, 12 percent is still an excellent average growth rate.

Difference in Salary for Machine Learning Master’s vs PhD

There is a significant contrast in earnings between a Machine learning PhD and a Machine learning Master’s degree. Although PayScale does not list the salary of Machine learning graduates specifically, it lists salary information for artificial intelligence, a field closely related to machine learning.

The average salary of an artificial intelligence PhD graduate is $115,000, while an AI master’s degree graduate earns an average salary of $103,000, annually . As you can see, a PhD will get you a very high average annual wage, and your salary can increase depending on your experience, location, and position.

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Why You Should Get a PhD in Machine Learning

You should get a PhD in machine learning because it will open up new job opportunities, help you earn a higher salary, and allow you to add value to the machine learning industry. If you enjoy doing research, learning new things, and want to earn a higher salary, then a PhD is perfect for you.

Reasons for Getting a PhD in Machine Learning

• Higher salaries. Earning a PhD ensures that you will get a job with a high-paying salary. A PhD is the highest degree level that you can achieve, and PhD graduates earn a significantly higher salary than associate, bachelor’s, or master’s degree holders.
• Contributing to your professional industry. While completing a PhD, students conduct a lot of original research, broaden their skills and add value to their field. At the end of a PhD, students submit a dissertation, a document that identifies a problem within the industry and presents a solution through research.
• Enhancing specialized and soft skills. A PhD will help you improve and gain valuable specialized skills and techniques in machine learning, such as statistics and natural language processing. You will also gain excellent soft skills in machine learning, like problem-solving and time management.
• Increasing job opportunities. Once you earn your PhD, your job opportunities will increase. A PhD will help you get a senior profession, such as a lecturer or senior machine learning engineer. According to PayScale, a senior machine learning engineer earns an annual wage of $153,255 .
• Gaining valuable knowledge. Due to a PhD’s research-intensive nature, students constantly learn new things and gain valuable knowledge. If you enjoy learning, you should get a PhD because the learning opportunities are endless.

Getting a PhD in Machine Learning: Machine Learning PhD Coursework

Getting a PhD in Machine Learning requires taking specific courses to meet the necessary credit hours to graduate from your PhD program. Required courses typically include machine learning, introduction to AI, and statistical learning. Machine learning PhD coursework will help you gain essential machine learning skills and knowledge.

During the machine learning course, students will learn about the fundamental topics and techniques in machine learning, such as logistic regression, clustering, classifications, deep neural networks, linear models, and support vector machines. This course encourages reinforcement learning by looking at several real-world examples.

Deep Learning

Deep learning is an essential part of machine learning and involves artificial neural networks. The deep learning course will teach students about theoretical and practical aspects of deep learning, including neural networks, optimization algorithms, and structured models.

Statistical Learning

This course will cover modern learning algorithms such as variational approximations, boosting, and support vector machines. While completing the statistical learning course, students will learn about statistical algorithms for data analysis and applications of signal processing. Students should know programming languages to enroll in this course.

Introduction to Artificial Intelligence

While completing a PhD in machine learning, students will need to complete an Artificial Intelligence course. An Introduction to AI course involves the study of models and theories related to systems that emulate human intelligence. Students will cover Bayesian networks, constraint satisfaction, probabilistic reasoning, and natural language processing.

Analysis of Algorithms

The analysis of algorithms course looks at different efficient algorithms and studies their complexity and correctness. Topics covered include network flow, dynamic programming, and amortized analysis. Students will discuss problems with no solutions and cover all different kinds of algorithms.

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How to Get a PhD in Machine Learning: Doctoral Program Requirements

Read the list below to find out how to get a PhD in Machine Learning. There are specific criteria that each student needs to meet before being awarded their degree. Common requirements include the completion of coursework, a research project, and a final thesis.

A machine learning PhD usually requires 40 to 48 credit hours. Students must take about six core courses and one elective course. During the first four semesters of their programs, students need to complete a specific number of credits before the next stage of their PhD.

Research is a considerable part of a PhD, so most programs will require students to take one or more responsible conduct of research courses. The responsible conduct of research courses involves seminars and workshops that help students learn the best methods of conducting research. Some research courses involve a project that will help students learn through practice. 

Machine learning PhDs will include a research project after completing the required research courses. The research project will be directed by a faculty member and requires students to conduct research and write a report. Students will then present their reports to the PhD committee. Research projects usually focus on a specific topic within machine learning or computer science.

Once students have completed the core course requirements and written their research project, they must complete a qualifying exam which typically includes an oral examination. The PhD committee sets the qualifying exam and is designed to assess whether students are ready to conduct independent research for their PhD thesis.

You need to act as a teaching assistant for two semesters in a machine learning course. This is a requirement that only some PhD programs have. The graduate chair and coordinator set the requirements of the teaching practicum.

The PhD thesis requires a few years of research around a specific topic in machine learning. Students research a particular topic, and then they need to present their findings to the PhD committee. The thesis also includes a defense of the dissertation. Usually, students need to submit a thesis draft to the committee for approval before defending it.

Potential Careers With a Machine Learning Degree

[query_class_embed] how-to-become-a-*profession

PhD in Machine Learning Salary and Job Outlook

Machine learning PhD graduates earn a highly favorable salary because a PhD is the highest degree level someone can earn. As stated above, PayScale does not list the average salary of a machine learning PhD graduate, but it notes that the average salary of an AI PhD graduate is  $115,000. Artificial intelligence is a field very closely related to machine learning.

The job outlook for a machine learning PhD graduate is between 12 and 22 percent. That is a very favorable job outlook. The BLS has stated that there are approximately 33,000 machine learning jobs each year.

What Can You Do With a PhD in Machine Learning?

With a PhD in machine learning, you can become a computer and information research scientist, a deep learning research engineer, or a computational linguist. Most higher education institutions offer career coaching services that help students prepare for interviews, write resumes, and find jobs. Contact your college to find out whether it offers career services.

Best Jobs with a PhD in Machine Learning

• Computer and Information Research Scientist
• Machine Learning Engineer
• Deep Learning Research Engineer
• Professor of Machine Learning
• Computational Linguist

What Is the Average Salary for a PhD in Machine Learning?

The average salary for a PhD in machine learning is $115,000 per year . This is a high average salary, but it varies based on factors such as experience, location, and job description. The more experience you have and the higher your degree level is, the higher your salary will be. If you decide to become a computer and information research scientist, you can earn an average salary of $131,490. If you are part of the 90th percentile, you can earn more than $208,000 annually .

Highest-Paying Machine Learning Jobs for PhD Grads

Best Machine Learning Jobs with a Doctorate

Now that we have looked at all the details about a machine learning PhD and how to become a machine learning engineer , let’s look at the five highest-paying machine learning Jobs for PhD graduates, in detail.

A machine learning engineer develops artificial intelligence systems that research and create algorithms that use large datasets. These algorithms can learn and make accurate predictions. Machine learning engineers are very skilled at programming, and they use programming languages like Java and Python.

• Salary with a Machine Learning PhD: $112,513
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, Washington

Deep learning research engineers use deep learning platforms to create programming systems that copy brain functions. They do this using neural networks, which have a similar structure to the human brain. These programming systems are designed to learn without the help of humans.

• Salary with a Machine Learning PhD: $110,679

A computer and information research scientist improves and creates computer hardware and software using complex algorithms. They streamline these complex algorithms and enhance system efficiency. Computer and information research scientists' simplified algorithms lead to advancements in machine learning systems and other types of technology.

• Salary with a Machine Learning PhD: $100,384

Professors of machine learning usually teach students at a university or college. They will teach courses related to a specific field. In this case, they will teach courses related to machine learning. Professors at big institutions may also conduct research and experiments and publish original research. If you enjoy teaching you can become a professor of machine learning. 

• Salary with a Machine Learning PhD: $98,500
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: Alaska, New York, Utah, California, New Jersey

Computational linguists are a specific kind of computer scientist. They work with computers and teach computer systems how to understand human languages. They have excellent coding skills because they use programming languages to code. They also conduct computational linguistic research around a specific functional area or product line.

• Salary with a Machine Learning PhD: $80,330

Is a PhD in Machine Learning Worth It?

Yes, a PhD in Machine Learning is worth it. There are many excellent institutions that can help you earn a PhD in Machine Learning while providing valuable support from faculty members. Earning this type of degree can help you further your machine learning career.

If you pursue a PhD in machine learning, you will very likely add value to your industry with the research conducted during your dissertation. Completing a PhD takes many years and is research-intensive but completely worth it if you look at the jobs that use machine learning and the average PhD in Machine learning salary.

Additional Reading About Machine Learning

[query_class_embed] https://careerkarma.com/blog/machine-learning/ https://careerkarma.com/blog/online-machine-learning-courses/ https://careerkarma.com/blog/how-to-get-a-job-in-machine-learning/

PhD in Machine Learning FAQ

The cheapest PhD in machine learning is the PhD in Machine Learning and Data Science offered by University of California San Diego. The PhD in Machine Learning and Data Science tuition at University of California San Diego costs $11,442 per year for both residents and non-residents.

Many top companies hire machine learning PhD graduates, including Google, Microsoft, Adobe, PayPal, Amazon, IBM, and Duolingo. With a PhD in machine learning, you can land a job at one of these companies and earn a high salary.

Yes, there are many remote jobs available for machine learning graduates. A quick search on websites such as Indeed, Glassdoor, and LinkedIn can put you in touch with many possible machine learning jobs. Make sure you read the details of each job carefully before you apply.

Yes, you can get a job in machine learning with a bootcamp. Bootcamps are short, but they are  intensive and can teach you all the necessary skills to have a successful career in the machine learning industry. There are many excellent machine learning bootcamps to help you start your machine learning career.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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The AIML Residency Program invites experts in various fields to apply their own domain expertise to innovate and build revolutionary machine learning and AI-powered products and experiences. As AI-based solutions spread across disciplines, the need for domain experts to understand machine learning and apply their expertise in ML settings grows. The program aims to invest in the resident’s technical and theoretical machine learning development to help advance their professional careers.

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The AI explosion has made machine learning jobs hot commodities. But do you really need a Ph.D. to get one?

• Companies are hiring machine learning engineers as they seek to grow their  AI talent  pools.
• While some say the role requires a Ph.D., others say it doesn't need an advanced degree.
• One engineer even considers having a Ph.D. a "red flag." 

Companies are vying to hire the best machine learning engineers — some for salaries well over six figures — as they scramble to staff up as the AI sector booms. Many of these jobs ask for the applicant to have a Ph.D.

But earlier this week, members of the tech community pushed back on X, formerly Twitter, about whether an advanced computer science degree is really necessary for landing a coveted machine learning role.

"I don't wanna get a Ph.D. but wanna work as a Machine Learning Engineer," an X user wrote, kicking off a debate. "Dilemma of the Century."

I don't wanna get a PhD but wanna work as a Machine Learning Engineer. Dilemma of the Century — Tanay Mehta (@serious_mehta) January 23, 2024

In perhaps good news for the original poster, many who replied don't see not holding a doctorate as a barrier to entry.

In fact, Cristian Garcia, a machine learning engineer who works at Google's DeepMind AI division, wrote on X (in a post that was later deleted) that "A Ph.D. is an overkill or even red flag for an ML Engineer Role (IMO)."

Garcia, who says he doesn't have a college degree and is self-taught in machine learning, told Business Insider that Ph.D. programs don't always teach DevOps , data cleaning, data engineering, and skills related to backend work that are typically required to do the job.

"Knowing machine learning alone is far from enough," Garcia told BI. "In other words, the actual job is related to ML only tangentially."

A different X user, who claims to have a Ph.D. in computer vision, wrote that recruiters who see "Ph.D." in a job applicant's résumé might think the candidate lacks industry experience — and that they're too expensive and theoretical.

One respondent said a doctorate is only relevant for research, not machine learning engineering. Another even suggested that companies that list a Ph.D. as a hard requirement are most likely looking for researchers instead — "or don't know what they're looking for."

But not all techies think an advanced degree is unnecessary. An X user who claims to be a grad student in computer science said Ph.D. students can bring an innovative approach to real-world problems, which could be an asset to their employers.

Related stories

The discussion comes as employers and would-be workers assess which skills and education are most useful as the AI job market booms. Recruiters at tech companies big and small have said that job applicants applying for AI-related roles don't necessarily need advanced STEM degrees to be hired.

Chris Foltz, the chief talent officer at IBM, previously told BI that when hiring for AI roles, the tech giant focuses on "prioritizing skills and experiences" over "traditional degrees" if candidates can demonstrate their AI knowledge.

Similarly, Nvidia's vice president of global recruiting Lindsey Duran said that applicants from non-traditional backgrounds can stand out if they can clearly emphasize their career milestones, leadership capabilities, and the impact of their past projects.

Alex Shapiro, the chief people officer at Jasper AI, an AI startup, even said that employees with less conventional backgrounds may, at times, be more attractive to hire than those with technical degrees.

One X user's response to the original post pointed out that a Ph.D. is just one way to become a machine learning engineer. And one other suggests, " Try at a startup, they'll take the risk" on someone without a Ph.D.. Then "break into a good company with that experience under your belt."

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• Main content

PhD Studentship: Using Machine Learning to understand the Role of the Soil Microbiome in Carbon Sequestration SCI285

University of nottingham - computer science.

Supervisor : Hannah Cooper

Secondary Supervisor : Andy Neal (Rothamsted)

Subject Area : Soil Science, Computer Science

Research Title : Using machine learning to understand the role of the soil microbiome in carbon sequestration

Research Description :

Managing natural processes is one of the most practical and effective implementable approaches to removing CO2 from the atmosphere. It is imperative to measure carbon sequestered by natural means accurately, to understand process drivers and uncertainties and to accelerate nature-based carbon sequestration. Soil can store or sequester carbon through microbiological activity, providing a nature-based sink for CO2. However, poorly managed soils can release carbon as CO2 or methane (CH4) to the atmosphere - contributing to climate change and reducing soil health and fertility.   This project will develop machine learning (ML) platforms to monitor, quantify and reveal the processes underlying soil carbon sequestration. This approach combines measurements of physical, chemical, and biological functional and evolutionary processes. Soil microbiome research focuses on determining which microbial taxa and functions facilitate carbon capture across a range of climatic conditions. There will be an analytical challenge to integrate datasets of different types, scales and modalities. These relate to the processing and integration of soil chemistry, soil structure (tomographic imaging data) and metagenomic profiling of soil microbiome across different environmental conditions and soil textures. The overall aim is to integrate disparate measurements of physical, chemical, and biological processes in soil to develop a generalizable predictive model of carbon sequestration.

Award Start Date : 01/12/2024

Duration of Award : 48 months

Terms and Conditions :

This research studentship is only available to UK citizens and includes payment of tuition fees and a tax-free stipend based on EPSRC rates

Applicant Qualification Requirements :

BSc degree in relevant science discipline

How to Apply :

email to [email protected] via the  ‘Apply’ button above.

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