phd in machine design

RESEARCH @ MIT MECHE

Design, manufacturing, and product development.

Design research investigates the complete set of activities involved in the process of bringing new devices, technologies, and services to the marketplace.

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Design and Manufacturing

In the Design research area, everything from a steam turbine to a gaming console is conceived, designed, fabricated, assembled, and delivered by an engineer who understands design, manufacturing, sustainability, and the supply chain.

Research Includes: Precision and machine design, product design and development, environment and sustainability, information and sensing, manufacturing process, and systems.

Design And Manufacturing News + Media

Visual Thinking for Engineers

Professor Maria Yang has discovered valuable strategies and techniques for designing both consumer products and complex engineering systems, partnering with NASA, Ferrari, and IBM.

Designing cleaner vehicles

Fueled by curiosity, second-year graduate student Adi Mehrotra ’22 is working on sustainable solutions in vehicle design.

The MIT Bike Lab: A place for community, hands-on learning

MIT's Bike Lab, an all-volunteer student-run bike shop, founded by graduate student Bianca Champenois, provides repair and maintenance services, emphasizes hands-on learning, and promotes sustainable transportation.

Design And Manufacturing Lab Spotlight

Visit our Design And Manufacturing lab sites to learn more about our faculty’s research projects.

• Biomimetic Robotics Lab
• Computer-Aided Design Laboratory
• Global Engineering Research Lab
• Laboratory for Manufacturing and Productivity
• Park Center for Complex Systems
• Zhao Laboratory

Meet Some of Our Faculty Working On Design And Manufacturing Challenges

MechE faculty are passionate, out-of-the-box thinkers who love to get their hands dirty.

• bioengineering

Selected Course Offerings in Design And Manufacturing

Learn about the impact of our design research.

Research areas in MechE are guidelines, not boundaries. Our faculty partner across disciplines to address the grand challenges of today and tomorrow, collaborating with researchers in MechE, MIT, industry, and beyond.

• Impact Health
• Impact Environment
• Impact Innovation
• Impact Security
• Impact Energy

Search Utah State University:

Mechanical engineering - ms, phd.

About This Degree

Mechanical engineering deals with the creation of the mechanical systems and machines that serve society. Mechanical engineers are involved in researching and designing mechanical devices of all types, including engines, tools, and machines. The broad discipline allows one to work in nearly every area of industry.

Utah State’s Department of Mechanical and Aerospace Engineering is recognized at both regional and national levels. MAE graduates consistently finish in the top 10 percent in intercollegiate national student design competitions. In 2009, the USU Unmanned Aerial Vehicle Team took first place at an international competition.

Graduate students enrolled in the program have the opportunity to study with award-winning faculty. The MAE Department received the university’s 2010 Department Teaching Excellence Award for outstanding teaching throughout the department.

What You Will Learn

The department is particularly strong in the following areas of study:

• olid mechanics is concerned with the mechanics of displacement and stress analysis combined with material science for selection of an optimum design. Included are studies of elasticity, plasticity, and failure in traditional metals and high-tech composite materials.
• Thermal/fluids is concerned with the transport of mass, momentum, and energy in solids, liquids, and gasses. Included within its scope are the fundamental studies of thermodynamics, heat transfer, and fluid mechanics.
• Dynamics and control is concerned with describing and controlling the motion of mechanical systems. Included within its scope are the fundamental studies of dynamics, kinematics, vibrations, control theory, electromechanical systems, and machine design.
• Composite Materials is concerned with how these materials are used, critical manufacturing processes, design methods, testing and structural analyses of these complex systems.

Concurrent Bachelor’s/Master’s Program:

The department also offers a concurrent bachelor’s/master’s program, which allows USU engineering students to begin taking graduate classes during their senior year as an undergraduate and to complete requirements for both the bachelor's degree and the master’s degree concurrently over two years.

At a Glance

College: College of Engineering

Department: Mechanical and Aerospace Engineering Department

USU Locations:

• Logan campus

Faculty: View profiles of faculty members on the department directory .

Program Requirements

• Update this page

Career And Outcomes

Career opportunities.

Graduates may pursue careers in the following areas:

• Electronics
• Environmental engineering
• Food processing
• Heating and air conditioning
• Heavy equipment
• Machine tools
• Manufacturing
• Public utilities
• Solar energy

Aerospace Engineering Specialization

• Aircraft design and development
• Aircraft flight testing
• Spacecraft and space systems design
• Spacecraft trajectory design and analysis

Additionally, some MS students enter prestigious PhD programs in top-tier schools.

Job Outlook

Request for information and advising.

Email: [email protected] Office: ENGR 417 Phone: (435) 797-0330

I am not a current USU student

I have not applied or been admitted, even if I've taken a USU Concurrent Enrollment course

I am a current USU student

I have been admitted and plan to attend, or continue attending USU

USU Locations

LOGAN CAMPUS

Admission Requirements

Students must have a bachelor’s degree from an accredited institution in mechanical engineering, aerospace engineering, manufacturing engineering, or a closely related engineering discipline. Students who do not have a bachelor’s degree in an appropriate engineering discipline may be admitted with nonmatriculated status (not officially accepted to the program) and required to complete remedial requirements. Once these courses are completed, students may be officially admitted to the program.

Students must also be well acquainted with either the FORTRAN or C programming language.

Application Requirements:

• Complete the online application
• Pay the $55 application fee
• GRE scores are used by some faculty in choosing students and in funding decisions and are therefore strongly encouraged
• Have a 3.3 or higher GPA on your last 60 semester or 90 quarter credits
• Provide transcripts of all college/university credits
• Provide three contacts for letters of recommendation
• Statement of Purpose
• Curriculum Vitae

International students have additional admissions requirements .

The department has the following deadlines:

• Fall semester - last day of February
• Spring semester - last day of August
• Domestic applications are accepted after these deadlines, but students will not be considered for financial assistance.
• International applications will be considered only at the deadline dates so accepted students have time to allow for Visa applications.

PhD Qualifying Exams:

In addition to the course requirements for the degree, the PhD program requires that all students pass the PhD qualifying exams. Students must pass these three exams with 80% or higher by the end of the third semester in the program. The qualifying exams will be based on undergraduate-level coursework and will consist of individual subject area exams.

Plan Options

Students can receive the MS by pursuing one of three options:

• In the Plan A option, students complete graduate-level coursework and must write and successfully defend a thesis.
• The Plan B option requires the production of a paper or creative work of art and is expected to reflect equivalent scholarship standards as a thesis. A successful defense is also required.
• A third option, Plan C , does not involve a thesis or a defense meeting and is comprised of coursework only.

Doctoral Degree Plan Option(s)

• PhD degree beyond a Bachelor’s
• PhD degree beyond a Master’s

Financial Aid

A variety of funding opportunities are available on the graduate school website .

Take The Next Step

How to apply.

View our step-by-step guide on how to become an Aggie.

Request Information

Contact the School of Graduate Studies to ask questions or receive more information.

Cost and Funding

Calculate the cost of graduate school and learn about funding opportunities.

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Doctoral Programs in Computational Science and Engineering

Doctor of philosophy in computational science and engineering, program requirements, programs offered by ccse in conjunction with select departments in the schools of engineering and science.

The interdisciplinary doctoral program in Computational Science and Engineering ( PhD in CSE + Engineering or Science ) offers students the opportunity to specialize at the doctoral level in a computation-related field of their choice via computationally-oriented coursework and a doctoral thesis with a disciplinary focus related to one of eight participating host departments, namely, Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Earth, Atmospheric and Planetary Sciences; Materials Science and Engineering; Mathematics; Mechanical Engineering; or Nuclear Science and Engineering.

Doctoral thesis fields associated with each department are as follows:

• Aerospace Engineering and Computational Science
• Computational Science and Engineering (available only to students who matriculate in 2023–2024 or earlier)
• Chemical Engineering and Computation
• Civil Engineering and Computation
• Environmental Engineering and Computation
• Computational Materials Science and Engineering
• Mechanical Engineering and Computation
• Computational Nuclear Science and Engineering
• Nuclear Engineering and Computation
• Computational Earth, Science and Planetary Sciences
• Mathematics and Computational Science

As with the standalone CSE PhD program, the emphasis of thesis research activities is the development of new computational methods and/or the innovative application of state-of-the-art computational techniques to important problems in engineering and science. In contrast to the standalone PhD program, however, this research is expected to have a strong disciplinary component of interest to the host department.

The interdisciplinary CSE PhD program is administered jointly by CCSE and the host departments. Students must submit an application to the CSE PhD program, indicating the department in which they wish to be hosted. To gain admission, CSE program applicants must receive approval from both the host department graduate admission committee and the CSE graduate admission committee. See the website for more information about the application process, requirements, and relevant deadlines .

Once admitted, doctoral degree candidates are expected to complete the host department's degree requirements (including qualifying exam) with some deviations relating to coursework, thesis committee composition, and thesis submission that are specific to the CSE program and are discussed in more detail on the CSE website . The most notable coursework requirement associated with this CSE degree is a course of study comprising five graduate subjects in CSE (below).

Computational Concentration Subjects

Note: Students may not use more than 12 units of credit from a "meets with undergraduate" subject to fulfill the CSE curriculum requirements

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Design & Manufacturing - Mechanical Engineering - Purdue University

Design & Manufacturing    

If you want to build it, first you’ve got to design it. That’s why Design & Manufacturing is such a vital aspect of engineering research at Purdue, discovering the ideals for mechanical systems, computational models, and human ergonomics.

Human beings and machines are interacting in new and unique ways in the 21st century. In one Purdue lab, researchers use toys and video games as a vehicle to study how humans utilize creativity in the smartphone era.  In another, faculty are studying materials at the nanoscopic level, to determine how best to manufacture the nanomaterials of the future.  Another lab compares traditional manufacturing techniques with open-source culture, mapping out new paradigms for social and technical systems.

Design also collaborates with and strengthens other areas of engineering, like biomechanics, robotics, manufacturing, and vehicles.

Purdue collaborates with Navajo Tech on manufacturing research

Purdue and collaborators 'put a flag in the ground' for in-space manufacturing

Purdue team wins "blue sky" award for the future of manufactured meat

Designing 3D food printers with frugal engineering

Purdue teams up with 3M to produce PPE

Sticktronics: Sometimes it's good to be thin-skinned

Shape Structuralizer streamlines the making process

Stacking chips to bring higher-performance computing

Toy Fair showcases Boilermaker fun

Faculty in Design & Manufacturing

Andres arrieta.

• Adaptive structures
• Mechanical metamaterials
• Robotic materials
• Programmable structures
• Multistable structures
• Structural nonlinearity
• Elastic instabilities
• Structural dynamics
• Nonlinear vibrations

Shubhra Bansal

• Renewable Energy Materials (physics-based energy yield predictions, sustainable PV and energy storage materials, recycling)
• Electro-Optical-Thermo-Mechanical Reliability (in-situ and in-operando accelerated stress tests)
• Heterogeneous Integration & Advanced Packaging (sub-10 μm pitch interconnects, low-loss interposers)
• Harsh Environment Electronics Integration (high temperature Pb-free solders and nano-thermal interfaces)

Ilias Bilionis

• Uncertainty propagation
• Inverse problems
• Propagation of information across scales
• Optimal learning
• Materials by design

Laura Blumenschein

• Growing robots
• Soft robotics
• Bioinspired systems
• Wearable robots
• Soft matter

Adrian Buganza Tepole

• Predictive computational tools for biological adaptation processes
• Tissue expansion
• Wound healing
• Reconstructive surgery optimization
• Numerical methods for biological membranes

Mukerrem Cakmak

• Modeling and experimental studies on processing
• Structure property relationships in polymer films and moldings and polymer/metal/ceramic hybrid systems

David Cappelleri

• Multi-scale robotic manipulation and assembly
• Mobile micro/nano robotics
• Micro/nano aerial vehicles
• Micro-Bio robotics
• Mechatronics
• Automation for the life sciences

George Chiu

• Dynamic systems and control
• Digital and functional printing and fabrication
• Motion and vibration control and perception
• Embedded systems and real-time control

Alex Chortos

• Bio-inspired and mechanically adaptive electronics
• Multimaterial additive fabrication
• Soft actuators (artificial muscles)
• Wearable actuators (haptics)
• Polymer design and polymer physics
• Deformation sensors and transistors

Raymond Cipra

• Mechanical systems design
• Analysis and simulation
• Computer aided engineering
• Robotics and automation

Hamid Dalir

• Composites materials design and manufacturing
• Sustainable and recyclable-by-design polymers and composites
• Polymer processing and characterization
• Composites recycling
• Hybrid manufacturing systems
• Multiscale modeling
• Damage mechanics

Xiaoping Du

• Design optimization
• Probabilistic and statistical methods
• Reliability-based and robust design
• Uncertainty quantification for machine learning

Greg Jensen

James Jones

• Cooperative learning
• Active noise and vibration control
• Smart materials
• Intelligent structures

Klod Kokini

• Thermal stresses, thermal fracture and fatigue of advanced materials, in particular high temperature materials, ceramic coatings.
• Mechanical behavior, design and remodeling of biological tissues, effect of stresses on remodeling, microbiomechanics of cell-extracellular matrix (ECM) interactions, tissue engineering

Chi Hwan Lee

• Wearable biomedical devices
• 'Crack’-driven transfer printing technology
• Scalable manufacturing technology
• Mechanics and materials for flexible/stretchable electronics

Heather Liddell

• Sustainable manufacturing
• Environmental life cycle assessment
• Mechanics of multilayered systems
• Adhesion in paints and coatings
• Lightweighting strategies for transportation

Nina Mahmoudian

• Marine Robotics
• Unmanned Systems
• Energy Autonomy
• Systems Design
• Coordination and Controls

Ajay Malshe

• Bio-inspired designs
• Surface engineering and multifunctional materials
• Convergent Manufacturing for Industry 5.0: hybrid manufacturing processes, heterogeneous materials, and bio-inspired designs
• Systems integration, productization, and production
• Heavy-duty machines: machining, lubrication, and corrosion
• Heterogeneous and hierarchical integration (mechanical-electrical-optical and nano-micro-meso-macro)
• Precision agricultural and food: cellular agriculture, vertical farming, micro-production, and resilience
• Frugal engineering, social innovations, and social equity
• Manufacturing in space

Jitesh Panchal

• Computational Design of Socio-Technical Systems
• Secure Design and Manufacturing
• Engineering Design by Self-Organized Virtual Communities
• Integrated Products and Materials Design

Gordon Pennock

• Kinematic synthesis and analysis
• Multi-degree-of-freedom mechanisms

Karthik Ramani

• Human Skill and Augmentation
• Collaborative and Hybridized Intelligence
• Deep Learning of Shapes and Computer Vision
• Human-Robot-Machine Interactions
• Making to Manufacturing (M2M)
• Factory of the Future and Robotics
• Manufacturing Productivity

Farshid Sadeghi

• Contact mechanics
• Stresses, fatigue and friction of rolling/sliding
• Micro-mechanics of boundary and mixed lubrication regimes
• Spall initiation and propagation
• Surface science and damage
• Dynamics of ball and rolling element bearings and rotating systems
• Friction induced vibration and squeal in dry contacts
• Friction and wear of dry and lubricated contacts
• Virtual tribology
• Dry and lubricated fretting wear
• MEMS for in-situ monitoring of tribological contacts
• Discrete element modeling

• Multi-process/multi-material additive manufacturing
• Nondestructive evaluation
• Advanced acoustic materials and metamaterials
• Ultrasonics

Ganesh Subbarayan

• Computational and experimental solid mechanics focused on fatigue, fracture, and multi-physics phase evolution problems
• Computational techniques including Finite Element Analysis (FEA), Isogeometric Analysis (IGA), geometric modeling, CAD and optimal design
• Heterogeneous Integration and Advanced Electronics Packaging with a focus on thermomechanical behavior, reliability, and electrical-thermal-mechanical co-design

• Multiscale superfast 3D optical sensing
• Biophotonic imaging
• Optical metrology
• Machine/computer vision
• 3D video telepresence
• 3D video processing
• Virtual reality
• Human computer interaction

• Environment friendly design and life cycle engineering
• Applications of bio-based materials in manufacturing
• Fast and low-cost detection of pathogenic microorganisms
• Biomass thermo-chemical upgrading for liquid and gaseous fuel

• Deformation, stress, plasticity, fracture
• Multiscale modeling, first-principles, molecular dynamics simulations, and finite element modeling
• In-situ experiments
• Mechanics of redox active materials - Li-ion batteries, Na-ion batteries, all-solid-state batteries
• Mechanics of polymeric materials - organic electrochromics, superelastic organic semiconductors

I want to research in these fundamental areas...

I want to have an impact in....

Mechanical Engineering

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Systems Design (Ph.D.)

Why pursue a Ph.D. in s ystems d esign at UNH?

The s ystems d esign program at UNH will help you address contemporary engineering and scientific problems that can be solved only through the cooperation of a variety of disciplines. Our interdepartmental program is aimed at developing scientists / engineers with management ability and engineers with system analysis and design capability. You will learn to make creative techni cal contributions to complex and to large-scale systems . You will work closely with a n interdisciplinary guidance committee to develop a course of study that addresses core degree requirements while also being tailored to your specific focus area s . Systems d esign engineers are in high demand in academia, research , industry and management .

Program H ighlights

Our program offers two professional directions : y ou will be able to concentrate on develop ing the skills and technical expertise to work with and direct groups of people on large-scale technical projects , or y ou can build and develop your capabilities in the theory and analysis of large-scale complex systems. You’ll work across varying departments with award-winning researchers , many of whom have received prestigious National Science Foundation CAREER awards. You’ll have access to world-class laboratories including the John Olson Advanced Manufacturing Center, the indoor underwater facilities at the Jere A. Chase Ocean Engineering Laboratory, and the Flow Physics Facility , which houses the largest boundary-layer wind tunnel in the world.

Potential career areas

• B iomedical e ngineering
• Communications
• Environment
• Information technology
• Large- s cale d esign and m anagement
• Machine l earning and i ntelligence
• Policymaking
• Robotics and a utomation
• Transportation

May-Win Thein

Request information.

Contact Information

Curriculum & Requirements

Program description.

The systems design doctoral degree is an interdepartmental program that addresses contemporary engineering and scientific problems that can be solved only through the cooperation of a variety of disciplines. Students in systems design can elect one of two professional directions. The first develops professionals with the technical expertise of a Ph.D. and with the ability to work with and direct groups of people working on large-scale technical projects. The second direction develops engineers with capabilities in the theory and analysis of large-scale complex systems. Concentration in an area of specific individual interest is combined with participation in a larger interdisciplinary project.

Requirements for the Program

Degree requirements.

Following entrance into the program, a guidance committee is appointed for the student by the dean of the Graduate School upon recommendation of the student's area coordinator. This committee assists the student in outlining a program and may specify individual coursework requirements in addition to those required by the area of specialization. The committee also conducts an annual in-depth review of each student's progress and, following substantial completion of a student's coursework, administers the qualifying examination. This committee is also responsible for administering the language examination and/or research-­tool proficiency requirements. Coursework and language requirements should normally be completed by the end of the second year of full­-time graduate study and must be completed before the student can be advanced to candidacy. Typically, at least 13 courses beyond the Bachelor of Science degree are required.

Upon the successful completion of the qualifying examination and other proficiency requirements, the student is advanced to candidacy and, upon the recommendation of the student's area coordinator, a doctoral committee is appointed by the dean of the Graduate School. The doctoral committee conducts an annual review of the student's progress, supervises, and approves the doctoral dissertation, and administers the final dissertation defense.

To obtain a Ph.D. degree, a student must meet all of the general requirements as stated under academic regulations and degree requirements of the Graduate School. Students are normally expected to take coursework equivalent to two full-time academic years beyond the baccalaureate and to complete a dissertation on original technical research that will require at least one additional year of full­-time study.

Student Learning Outcomes

• A deep understanding of at least one core area within CEPS.
• A broader understanding of at least 1 other area of CEPS (within the same department or another department) or another college that is/are different from the core area of research and that is/are necessary to complete the student’s multi-disciplinary research.
• Ability to think critically and creatively in defining research questions and to outline strategies of inquiry.
• Ability to combine the knowledge and skills across multiple disciplines to solve a complex and/or large-scale problem.
• Ability to document research outcomes comprehensively for publication.
• Ability to communicate research results to scientific audience in conferences.
• Ability to work collaboratively with other peers.

Application Requirements & Deadlines

Applications must be completed by the following deadlines in order to be reviewed for admission:

• Fall : Feb. 15 (for funding); April 1 (recommended US; final international); July 1 (final)
• Spring : Dec. 1; Nov. 1 for international students
• Summer : N/A
• Special : N/A

Application fee : $65

Campus : Durham

New England Regional : No

Accelerated Masters Eligible : No

New Hampshire Residents

Students claiming in-state residency must also submit a Proof of Residence Form . This form is not required to complete your application, but you will need to submit it after you are offered admission or you will not be able to register for classes.

Transcripts

If you attended UNH or Granite State College (GSC) after September 1, 1991, and have indicated so on your online application, we will retrieve your transcript internally; this includes UNH-Durham, UNH-Manchester, UNH Non-Degree work and GSC. 

If you did not attend UNH, or attended prior to September 1, 1991, then you must upload a copy (PDF) of your transcript in the application form. International transcripts must be translated into English.

If admitted , you must then request an official transcript be sent directly to our office from the Registrar's Office of each college/university attended. We accept transcripts both electronically and in hard copy:

• Electronic Transcripts : Please have your institution send the transcript directly to [email protected] . Please note that we can only accept copies sent directly from the institution.
• Paper Transcripts : Please send hard copies of transcripts to: UNH Graduate School, Thompson Hall- 105 Main Street, Durham, NH 03824. You may request transcripts be sent to us directly from the institution or you may send them yourself as long as they remain sealed in the original university envelope.

Transcripts from all previous post-secondary institutions must be submitted and applicants must disclose any previous academic or disciplinary sanctions that resulted in their temporary or permanent separation from a previous post-secondary institution. If it is found that previous academic or disciplinary separations were not disclosed, applicants may face denial and admitted students may face dismissal from their academic program.

Letters of recommendation: 3 required

Recommendation letters submitted by relatives or friends, as well as letters older than one year, will not be accepted.

Test Scores: GRE Required

GRE required. Request official test scores to be sent directly to the Graduate School by the testing service. Test scores more than five years old are not acceptable. Student copies and photo copies of scores are not considered official. Our CEEB code is 3918.

For general information about test scores, including waiver requests and current COVID related impacts,  please visit our Test Scores webpage .

Personal Statement/Essay Questions

Prepare a brief but careful statement regarding:

• Reasons you wish to do graduate work in this field, including your immediate and long-range objectives.
• Your specific research or professional interest and experiences in this field.

Important Notes

All applicants are encouraged to contact programs directly to discuss program-specific application questions.

International Applicants

Prospective international students are required to submit TOEFL, IELTS, or equivalent examination scores. English Language Exams may be waived if English is your first language. If you wish to request a waiver, then please visit our Test Scores webpage for more information.

Explore Program Details

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Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

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This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

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A non-degree, customizable program for mid-career professionals.

PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

Admissions Events

Check out our event schedule, and learn when you can chat with us in person or online.

Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

June phd program overview.

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

July PhD Program Overview

August phd program overview, september 12 phd program overview.

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education. Here are the academic placements for our PhDs graduating in May and September 2024. Our 2024-2025 job market candidates will be posted in early June 2024.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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Machine Design (Implemented From July 2024)

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Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2023 for a start date of August 2024 in the Machine Learning PhD program, GRE Scores are REQUIRED. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2021, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2018, will not be accepted.)

Graduate Online Application

• Early Application Deadline – November 29, 2023 (3:00 p.m. EST)
• Final Application Deadline - December 13, 2023 (3:00 p.m. EST)

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• AE 8803 Gaussian Processes
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6402 , Time Series Analysis
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6221 , Probability Theory for Scientists and Engineers
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• AE 8803 , Optimal Transport Theory and Applications
• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6241 , Probability I
• MATH 6262 , Advanced Statistical Inference
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 6790 / ECE 6790 , Information Processing Models in Neural Systems
• BMED 7610 , Quantitative Neuroscience
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CHBE 6745 , Data Analytics for Chemical Engineers
• CHBE 6746 , Data-Driven Process Engineering
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7647 , Machine Learning with Limited Supervision
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• AE 6513 , Mathematical Planning and Decision-Making for Autonomy
• AE 8803 , Optimization-Based Learning Control and Games
• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ECE 6553 , Optimal Control and Optimization
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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PhD Machines and Equipment Design

Programme details.

The doctoral study programme Machines and Equipment Design is focused on independent scientific and research projects in the field of the design of machines and equipment. Its aim is to produce highly-educated and creative graduates capable of taking part in scientific, research, and professional activities. The Ph.D. candidates further extend and refine their knowledge of mathematics, physics, and applied sciences in the theoretical foundation of the study discipline to be able to succeed in solutions of tasks in the fields of development and design, modelling and simulations of operations and design and performance of experiments.

The graduates are able to independently apply the mastered skills and the high level of theoretical knowledge provided in lectures and acquired through modern learning methods, e. g. calculations and experiments, for solving specific problems in the selected areas of the design of parts and mechanisms of machines, wheeled transport, handling machines, working and assembling machines, piston combustion engines, glass-making machines and robotics, textile machines and machines for the production of nanofibres, equipment for thermal technologies and for technical diagnostics, etc. Furthermore, they are familiar with the research methods in the field of the design of machines and equipment and capable of critical analysis, evaluation, and synthesis, thus able to discuss the results of their investigations and to extend the sum of the current knowledge in a professional way worth of publishing and sharing with other members of the professional community both in their country and abroad.

After the completion of the study programme the graduates will succeed in positions of research team members in both theoretical and applied research areas or as experts in the teams of industrial development centers and major research institutions focusing on a particular area. Due to their profound knowledge and professional approaches, they will also succeed in a broader context as lecturers of tertiary education institutions.

More detailed information on the faculty website

Škoda Auto PhD Programme

PhD candidates at TUL are eligible to apply for participation in the Škoda Auto PhD programme or they can write their thesis on a topic offered by the company.

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PhD Program

The machine learning (ML) Ph.D. program is a collaborative venture between Georgia Tech's colleges of Computing, Engineering, and Sciences. Approximately 25-30 students enter the program each year through nine different academic units. 

ML@GT manages all operations and curricular requirements for the new Ph.D. Program, which include four core and five elective courses, a qualifying exam, and a doctoral dissertation defense .

See the curriculum overview for more information.

Students admitted into the ML Ph.D. program can be advised by any of our  participating ML Ph.D. Program faculty .

More information about admission to the ML Ph.D. program can be found here .

More information about the program itself, including details on operations and curriculum outlined in the ML Handbook, can be found in the current student resources.

ML@GT Ph.D. Faculty Advisory Committee

• Aerospace Engineering (AE): Evangelos Theodorou, [email protected]
• Biomedical Engineering (BME): May Wang, [email protected]
• Chemical and Biomolecular Engineering (ChBE): Martha Grover, [email protected]
• Computational Science and Engineering (CSE): Polo Chau, [email protected]
• Electrical Computer Engineering (ECE): David Anderson, [email protected]
• Computer Science (CS): Jacob Abernethy, prof.gatech.edu
• Industrial Systems Engineering (ISyE): Yao Xie, [email protected]
• Interactive Computing (IC): Zsolt Kira, [email protected]
• Mathematics (MATH): Vladimir Koltchinskii, [email protected]

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Best Doctorates in Human-Computer Interaction: Top PhD Programs, Career Paths, and Salaries

The use of human-computer interaction (HCI) learning systems and assistive technologies is growing in numerous exciting industries. In this article, we will cover 10 of the best PhDs in Human-Computer Interaction offered in the United States, as well as the PhD in Human-Computer Interaction salary opportunities and jobs in the interaction design field.

A PhD in Human-Computer Interaction is focused on interactive design and understanding how users interact with computers. This knowledge allows students to create research-backed digital interfaces for users based on their needs and preferences. These PhD degree programs will increase students’ level of experience and design thinking skills to make them experts in the computing and information technology field.

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What is a phd in human-computer interaction.

A PhD in Human-Computer Interaction (HCI) is a doctoral degree program that combines many different disciplines, including artificial intelligence, graphic design, assistive technologies, social computing, cognitive science, and interaction design thinking to create relevant computing and information technology that can solve real-world problems for technology users.

During an HCI PhD program, the graduate student will utilize different learning systems and collaborate with faculty advisors to define their area of interest and research in the field. No matter their future career goals, students will gain excellent skills in interactive systems and have the opportunity to perform cutting-edge research in this key industry.

How to Get Into a Human-Computer Interaction PhD Program: Admission Requirements

The admission requirements for prospective students of human-computer interaction PhD programs include a master’s or bachelor’s degree and official transcripts from previous academic programs they have pursued. Other requirements may include a statement of purpose that describes your primary research interests in the HCI academic field, a resume, and letters of recommendation.

English as a second language (ESL) and all international students will need to provide proof of English proficiency in the form of Test of English as a Foreign Language (TOEFL) exam scores or an equivalent exam. Individual programs may also require Graduate Record Examination (GRE) scores and application fees in order to be considered for admission.

PhD in Human-Computer Interaction Admission Requirements

• Master’s or bachelor’s degree in a related field
• Official transcripts
• GRE exam scores, depending on the school
• TOEFL exam or equivalent for ESL and international students 
• Online application and fee to be completed according to application deadlines
• Statement of purpose
• Letters of recommendation
• Current resume

Human-Computer Interaction PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Human-Computer Interaction?

It can be hard to get into a PhD in Human-Computer Interaction program. Doctoral studies that focus on interactive systems can be somewhat limited, so these programs are typically more challenging to get into as compared to programs with a social science focus, for example.

Acceptance rates will vary by the type of graduate study, a candidate’s previous academic progress, and the college’s reputation. You can research the general acceptance rates for each university, and the program may disclose acceptance information and how competitive the program is on the department’s website.

How to Get Into the Best Universities

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Best PhDs in Human-Computer Interaction: In Brief

Best universities for human-computer interaction phds: where to get a phd in human-computer interaction.

The best universities for human-computer interaction PhD programs include the desirable Stanford University, Indiana University, and Carnegie Mellon University. Continue reading below for an overview of each human-computer interaction PhD degree program and relevant details like tuition costs, application details, and funding options.

Arizona State University (ASU) is a public research university founded in 1885. ASU enrolls over 25,000 graduate students that come from all walks of life. Among public universities, ASU is considered an innovative university as it offers many popular graduate programs, including information and technology, information systems, supply chain management, and engineering programs. 

PhD in Human Systems Engineering 

The 84-credit-hour program in Human Systems Engineering will help you launch your academic and professional career in the exciting interaction design field. Subjects and research areas of this degree track include digital technologies, mobile computing, and advanced computing. students will also complete a dissertation and faculty-guided research. 

PhD in Human Systems Engineering Overview

• Program Length: 5 to 6 years
• Acceptance Rate: N/A
• Tuition and Fees: $11,720/year (in state); $23,544/year (out of state)
• PhD Funding Opportunities: Dean’s Fellowship, Interdisciplinary Enrichment Fellowship, Graduate College Fellowship 

PhD in Human Systems and Engineering Admission Requirements

• Bachelor’s Degree in Cognitive Science, Computer Science, Engineering, or a related field
• 3.0 minimum GPA
• GRE exam scores
• Three letters of recommendation 
• Working knowledge of cognitive science and statistics 

Carnegie Mellon is a private university in Pittsburgh founded in 1900 by Andrew Carnegie. Carnegie Mellon University has one of the best computer science degree programs in the country and is currently ranked second on the list of the Best Computer Science Schools by the US News & World Report. The university offers full tuition aid and a stipend to accepted PhD students. 

PhD in Human-Computer Interaction 

An HCI student in this PhD program studying full time will take two and half years to complete the required credit hours for coursework. Current students can choose to conduct their dissertation research in behavioral science, product design, and user experience design. 

HCI graduates will have a broad range of expert-level skills to qualify for work in digital media, interaction design, and social sciences, or more research-based and technical industry positions. 

PhD in Human-Computer Interaction Overview

• Program Length: 3 to 5 years
• Acceptance Rate: 8%
• Tuition: $46,400/year
• PhD Funding Opportunities: Full tuition coverage and a yearly stipend; NSF Graduate Research Fellowship, Microsoft Research PhD Fellowship, Siebel Scholars Program
• Bachelor’s or master’s degree
• Video essay

Clemson University was founded in 1889 as a public land-grant university. A few of the most popular graduate programs offered by the college include biomedical engineering, computing, nursing, and digital media. Clemson is ranked 75th for best national universities and 30th for top public schools, according to US News & World Report. 

PhD in Human-Centered Computing 

The 60-credit hour program in human-centered computing features research and dissertation opportunities and will effectively prepare prospective students for a career path in academia, user experience design, and many other key industry-specific positions. 

Prospective students have the option of enrolling in Clemson’s dual degree program and can take coursework that counts toward both an MS and PhD degree. 

PhD in Human-Centered Computing Overview

• Program Length: 3 to 6 years
• Tuition and Fees: $5,654/semester (in state); $11,242/semester (out of state)
• PhD Funding Opportunities: Graduate assistantships, NSF Graduate Research Fellowship Program, NVIDIA Fellowship, Microsoft Research PhD Fellowship, Facebook Fellowship

PhD in Human-Centered Computing Admission Requirements

• Bachelor or Master of Science in Computer Science, Engineering, or Mathematics
• 3.5 minimum GPA
• Personal statement
• Two letters of recommendation 
• Research writing sample
• Letter of support from an academic advisor from the department

Cornell University is a private land-grant university that was founded in 1865 by diplomat Andrew Dickson White. Cornell is an Ivy League school and is famous for being the first university to provide degree programs in journalism, electrical and industrial engineering, and veterinary medicine.

PhD in Information Science

The PhD in Information Science doctoral program is a cutting-edge research degree with a focus on human-computer interaction and technological systems. Prospective students who have research interests in areas like interactive design, mobile computing, computer modeling, and applied machine learning will thrive in this comprehensive university PhD curriculum. 

PhD in Information Science Overview

• Program Length: 5 years
• Acceptance Rate: 10%
• Tuition: $29,500/year 
• PhD Funding Opportunities: Graduate teaching and research assistantships, yearly stipends, Cornell fellowships, and numerous external fellowships

PhD in Information Science Admission Requirements

• $105 non-refundable application fee

Georgia Institute of Technology is a public research university founded in 1885. Georgia Tech is popular for its advanced computing and engineering colleges and has many of the highest rankings for its numerous technology programs. The innovative university is currently ranked sixth on the US News & World Report’s list of the Best Computer Science Schools. 

Georgia Tech’s PhD in Human-Centered Computing program requires graduate students to complete a minimum of 27 credit hours during their initial two and a half years of coursework. Core courses explore topics like human-centered computing (HCC) basics, prototyping interactive design systems, and advanced research seminar work in HCC. 

• Acceptance Rate: 17%
• Tuition: $586/credit hour (in state); $1,215/credit hour (out of state)
• PhD Funding Opportunities: ARPA-E Fellows Program, Microsoft Research PhD Fellowship Program, NASA Fellowships, and numerous additional external grants and fellowships
• Three letters of recommendation
• Examples of HCC research

Indiana University Bloomington is a public research university founded in 1820 and is the flagship university of Indiana University’s seven other campuses. The comprehensive university is known for its great student-to-faculty ratio and enrolls more than 8,500 graduate students. Indiana University was the first university in the US to offer an informatics PhD degree program. 

PhD in Informatics: Human-Computer Interaction/Design Specialization

This Luddy College of Engineering PhD program requires 90 credit hours of coursework. The requirement consists of 30 credits each for elective courses and dissertation work, 18 credits for core coursework, six credits each for seminar and research, and 12 credits for theoretical courses. 

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Graduate students of this program will gain expert-level skills in user-centered design, design pedagogy, interaction design and information architecture tools , and advanced computing during their human-computer interaction design track specialization. 

PhD in Informatics: Human-Computer Interaction/Design Specialization Overview

• Program Length: 5 to 7 years
• Tuition: $420/credit (in state); $1,330/credit (out of state)
• PhD Funding Opportunities: Associate Instructor/Research Assistant Appointment and Stipend, Tuition Fee Remission, UGS Grant-in-Aid PhD Research Award

PhD in Informatics: Human-Computer Interaction/Design Specialization Admission Requirements

• Statement of purpose 
• Resume 
• Official transcripts 

One of the world’s most desirable universities, the Massachusetts Institute of Technology (MIT) was founded in 1861 as a private research university. MIT’s most popular undergraduate and graduate programs include degrees in tech, engineering, and physical sciences. According to the US News & World Report, MIT ranks as the number one school for engineering, computer science, mathematics, and economics graduate programs in the United States.

PhD in Computer Science and Engineering  

The Schwarzman College of Computing at MIT offers a human-computer interaction degree program within the Electrical Engineering and Computer Science department. Students of this program will gain theoretical knowledge and excellent research skills in concepts like machine learning, artificial intelligence , mobile computing, and graphic information technology. 

PhD in Computer Science and Engineering Overview

• Tuition: $28,795/year (out of state)
• PhD Funding Opportunities: Graduate teaching and research assistantships, monthly stipends, and numerous MIT and external fellowships 

PhD in Computer Science and Engineering Admission Requirements

• Statement of objectives

Rochester Institute of Technology is a private research university founded in 1829. The university is known for its online and on-campus graduate programs in engineering, computer science, and business. Rochester Institute of Technology currently enrolls more than 19,000 undergraduate and graduate students. 

PhD in Computing and Information Sciences

The PhD in Computing and Information Sciences program at Rochester Institute of Technology (RIT) features a comprehensive, interdisciplinary 60-credit curriculum that focuses on subjects such as access technologies and computer-based instructional systems. 

Students will complete a dissertation, research, and core coursework in numerous disciplines, such as human-computer interaction, cyber infrastructure, social computing, and machine learning and AI. 

PhD in Computing and Information Sciences Overview

• Program Length: 3 to 4 years
• Tuition and Fees: $2,257/credit 
• PhD Funding Opportunities: Graduate research and teaching assistantships

PhD in Computing and Information Sciences Admission Requirements

• Bachelor’s Degree in Engineering, Science, or a related field 
• 3.0 minimum GPA recommended
• Personal statement 
• Professional portfolio 
• Two letters of recommendation
• Research paper samples 

Stanford University is a private research university founded in 1885 and is among the most prestigious universities in the United States. Stanford is highly competitive and is known for its popular graduate engineering and social sciences programs. The school currently hosts more than 8,000 graduate students and offers over 200 master’s and doctoral programs. 

PhD in Computer Science

The PhD in Computer Science degree program at Stanford offers students a chance to become experts in computing and features coursework in interactive design, prototyping, and interaction techniques. This degree program requires students to pass qualifying exams, conduct and orally defend a thesis and relevant research, and complete 135 course and research credits. 

PhD in Computer Science Overview

• Tuition and Fees: $66,297/year
• PhD Funding Opportunities: Graduate teaching and research assistantships, GEM Fellowship Program, numerous grant and scholarship opportunities

PhD in Computer Science Admission Requirements

The University of Washington (UW) was founded in 1861 as a public research university. UW is  one of the oldest universities on the West Coast and is known for its popular degrees in engineering, medicine, and business. The school offers over 300 programs to more than 12,000 graduate and doctoral students. 

PhD in Human-Centered Design and Engineering 

Prospective students of this human-computer interaction design track will learn about interaction design and information architecture, user-centered design, and the important interactive technologies used in everyday life. This PhD program requires a minimum of 90 total credits, including graduate school coursework, research, and dissertation work.

PhD in Human-Centered Design and Engineering Overview

• Acceptance Rate: 65%
• Tuition: $17,136/year (in state); $30,036/year (out of state)
• PhD Funding Opportunities: Graduate teaching and research assistantships, NSF Graduate Research Fellowship Program, GSEE graduate tuition award, numerous UW and external fellowships

PhD in Human-Centered Design and Engineering Admission Requirements

• Research summary with faculty matches 

Can You Get a PhD in Human-Computer Interaction Online?

Yes, you can get a PhD in Human-Computer Interaction online. However, online PhD programs in this field are rare, as most degrees are only offered on campus.

Auburn University and Dakota State University offer online PhD programs. Most HCI degrees are computer science programs and will offer certain core courses or specializations in human-computer interaction, digital media, and user interface interaction design .

Best Online PhD Programs in Human-Computer Interaction

How long does it take to get a phd in human-computer interaction.

It takes between three to six years to get a PhD in Human-Computer Interaction. A PhD student of an HCI degree program usually takes two years to finish their PhD coursework after having completed a master’s program in a related field.

After completing your HCI degree program coursework, the rest of your time will be spent conducting research and dissertation work on real-world problems that can be solved through HCI interactive design, analysis, and research.

Is a PhD in Human-Computer Interaction Hard?

Yes, a PhD in Human-Computer Interaction can be quite hard to complete. This type of PhD program requires knowledge of many interdisciplinary fields such as cognitive science, computing, mathematics, and statistics. Research interest in how humans and creative technologies interact and problem-solving skills in interaction design are crucial for the field.

Prospective students for graduate school in HCI who are scientifically aligned and have an aptitude for creative thinking and interaction design would make the ideal candidates for an HCI degree program. The hard work spent in a doctoral program will be more than worth it.

How Much Does It Cost to Get a PhD in Human-Computer Interaction?

It costs an average of $19,314 to get a PhD in Human-Computer Interaction, according to the National Center for Education Statistics. This value is the average tuition figure of all public and private institutions.

It is important to remember that tuition costs will depend on different factors, such as whether the university is private or public, whether you are a resident or non-resident, and what financial aid and funding opportunities are offered by the program.

How to Pay for a PhD in Human-Computer Interaction: PhD Funding Options

The PhD funding options that students can use to pay for a PhD in Human-Computer Interaction include full or partial tuition funding and fixed yearly or monthly stipend payments offered by the university through the means of teaching or research assistantships, grants, and fellowships. Other federal or state aid options are available, such as the Free Application for Federal Student Aid (FAFSA) and state-approved grants.

Best Online Master’s Degrees

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What Is the Difference Between a Human-Computer Interaction Master’s Degree and PhD?

The difference between a human-computer interaction master’s degree and a PhD is the length of study and the depth of each program’s curriculum. An HCI master’s program prepares a graduate student for their future goal of obtaining relevant employment and is usually two years in length.

A PhD human-computer interaction degree program is a longer commitment, taking between three to seven years to complete. It prepares students for academic pathways in HCI and gears them toward lifelong careers in academic research in the areas of interaction design, computing, and engineering. PhD students also tend to have more intellectual autonomy and can choose to conduct paid research in specific areas of interest.

Master’s vs PhD in Human-Computer Interaction Job Outlook

According to the US Bureau of Labor Statistics (BLS), the job outlook for PhD in Human-Computer Interaction degree holders is projected to grow by 22 percent by 2030 for those who become computer and information research scientists. This career path typically requires at least a master’s degree, but a PhD is preferred.

In comparison, the job outlook for those with HCI master’s degrees who become industrial engineers is projected to grow by 14 percent by 2030 . The job outlook for human-computer interaction jobs is generally better for PhD degree holders because they are the most qualified candidates by default and have more job opportunities than master’s degree holders.

Difference in Salary for Human-Computer Interaction Master’s vs PhD

The difference in the average salary for those with human-computer interaction master’s degrees vs PhDs is about $30,000 per year, according to PayScale. Those with a Master of Science in HCI earn an average base salary of $90,000 per year . Human-computer interaction PhD graduates, on the other hand, earn an average salary of $119,000 per year.

Related Human-Computer Interaction Degrees

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Why You Should Get a PhD in Human-Computer Interaction

You should get a PhD in Human-Computer Interaction because it will shape the future, specifically in the ways in which humans interact with digitally creative technologies. Human-centered interaction design a lucrative field with a rapidly growing job outlook, and obtaining a PhD will make you an expert in the field with unlimited room for career growth.

Reasons for Getting a PhD in Human-Computer Interaction

• Great job outlook. Getting a PhD in Human-Computer Interaction guarantees you a great career in terms of employment opportunities and job growth. According to BLS,  there are 3,200 openings for computer and information research scientists every year.
• High earning potential. According to BLS, the median pay after getting a PhD in Human-Computer Interaction is about $131,490 per year . Yearly salaries can be even higher, depending on the industry and your years of experience.
• Cutting-edge research. Pursuing a PhD in Human-Computer Interaction will put you at the forefront of cutting-edge technologies and research involving subjects like brain informatics, design pedagogy, interactive data exploration, and user-centered design.
• Wide range of career options. As human-computer interaction design is an interdisciplinary degree, getting a PhD in the subject means you are eligible to work in multiple exciting positions, such as UX research and interaction designer, human systems engineer, or human-computer research scientist roles.

Getting a PhD in Human-Computer Interaction: Human-Computer Interaction PhD Coursework

Getting a PhD in Human-Computer Interaction involves finishing mandatory human-computer interaction PhD coursework. Below you will find some of the most common courses taught in a PhD in Human-Computer Interaction curriculum.

Interaction Design

A PhD in Human-Computer Interaction typically involves a core subject in interaction design, in which graduate students learn how to design a digital experience, physical product, or service that anticipates and caters to the need of its users.

HCI Process and Theory

As a required part of the coursework during a PhD in Human-Computer Interaction design, students are taught the basic theories, methodologies, and processes involved in human-computer interaction design and engineering.

Social Computing

Students in this course will learn how digital technologies can support social interaction among human beings. It studies the intersection of computational technologies and online social behavior or interaction with digital systems.

Foundations of Applied Cognitive Science

This course explores the psychology and the cognitive science behind users’ needs, knowledge, and actions. It focuses on understanding the user’s psychology in terms of human-computer interaction to accurately predict, design, and achieve agreeable human-centered results.

Fundamentals of Human-Centered Computing

This subject is similar to applied cognitive science but focuses more on the behavior and actions of humans interacting with technologies. The course teaches computer systems engineering and methodologies which combine social science, cognitive science, and computing to fill technological gaps for users.

Best Master’s Degrees

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How to Get a PhD in Human-Computer Interaction: Doctoral Program Requirements

If you are still wondering how to get a PhD in Human-Computer Interaction, continue reading below to find out more about the doctoral program requirements for HCI graduate students to complete their PhD degrees.

To get a PhD in Human-Computer Interaction, you’ll have to fulfill the core course requirements. The course requirements for an HCI doctoral program usually include courses on interaction design, applied cognitive science, and social computing. 

Graduate students will usually have to complete one or two teaching assistantships during the duration of their PhD to fulfill the doctoral program requirements. These assistantships are typically paid and can help cover tuition costs. 

Doctoral students of computer science and human-computer interaction will have to complete required core and elective courses for their specialization, usually amounting to between 10 to 18 credit hours.

Graduate students are required to complete an internship or project, teach an introductory course on human-computer interaction, or take a Usability Methods course in HCI. This is done within the doctoral program so faculty and advisors can make sure that the doctoral student is ready for HCI-related work beyond the degree program.

Perhaps the most important requirement of a PhD in Human-Computer Interaction is for doctoral students to propose, conduct, report, and defend a PhD thesis in human-computer interaction. As a PhD student of HCI, you’ll have to propose an original thesis topic, collaborate with your PhD committee consisting of faculty members and advisors, and present and defend your thesis, both orally and in written form.

Potential Careers With a Human-Computer Interaction Degree

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PhD in Human-Computer Interaction Salary and Job Outlook

The salary and job outlook available after getting a PhD in Human-Computer Interaction significantly improves as compared to the job outlook of a bachelor’s or master’s program in human-computer interaction or human-centered design and engineering. A PhD opens doors to exciting careers and higher-paying jobs and makes you more qualified than most in the field.

What Can You Do With a PhD in Human-Computer Interaction?

With a PhD in Human-Computer Interaction, you can have your pick of career opportunities. You can choose to work solely in academia as a professor or researcher or get hired by large private corporations to work as a usability engineer, interaction and interface designer, or information architect.

Best Jobs with a PhD in Human-Computer Interaction

• Usability Engineer
• Information Architect
• Interaction and Interface Designer
• Visual Analyst
• UX Designer

What Is the Average Salary for a PhD in Human-Computer Interaction? 

The average salary for someone with a PhD in Human-Computer Interaction is $119,000 per year , according to PayScale. You can also earn up to $180,000 per year as a user experience researcher, a lead UX designer, or a senior product manager after obtaining a PhD in Human-Computer Interaction.

Highest-Paying Human-Computer Interaction Jobs for PhD Grads

Best human-computer interaction jobs with a doctorate.

Read on to learn more about the best human-computer interaction jobs available to those with a doctorate in HCI or a specialization in human-computer interaction under a computer science PhD program. Most of these jobs require at least a master’s degree, but PhDs are preferred.

A senior product manager takes care of the product strategy and roadmap of the product line. They are responsible for estimating the product’s business value, overseeing its design, and leading the product team from the product’s conception to its launch date.

• Salary with a human-computer interaction PhD: $159,010
• Job Outlook: 11% job growth from 2020 to 2030
• Number of Jobs: 482,000
• Highest-Paying States: New York, California, New Jersey

A user experience (UX) researcher is responsible for the testing and data analysis of the user’s experience. They are responsible for determining the needs of their users and better understanding their psychology through analytics and insights. A UX researcher works cross-functionally with designers, product managers, and engineers to ensure best practices.

• Salary with a human-computer interaction PhD: $131,490
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas

A human-computer interaction research engineer builds highly interactive user interfaces and systems. They create websites or product infrastructures and software applications and conduct experimental testing on user interfaces to improve the overall user experience. 

• Salary with a human-computer interaction PhD: $101,780
• Job Outlook: 7% job growth from 2020 to 2030
• Number of Jobs: 313,200
• Highest-Paying States: California, District of Columbia, Washington

A computer programmer is responsible for developing various software applications through full stack development. A computer programmer with a PhD in Human-Computer Interaction will be highly skilled and specialized in creating, testing, and improving research-backed user-centric interface designs. 

• Salary with a human-computer interaction PhD: $93,000
• Job Outlook: 10% job decline from 2020 to 2030
• Number of Jobs: 185,700
• Highest-Paying States: Washington, California, Virginia

A user experience (UX) designer creates the visual interface of a digital product. Their responsibilities include ensuring usability, clean infrastructure, accessibility, and ease of use in the interactive user interfaces they design. They also create storyboards and conduct testing and case studies to come up with the best design solution to fulfill user needs. 

• Salary with a human-computer interaction PhD: $77,200
• Job Outlook: 13% job growth from 2020 to 2030
• Number of Jobs: 199,400
• Highest-Paying States: Washington, New York, Iowa

Is a PhD in Human-Computer Interaction Worth It?

Yes, a PhD in Human-Computer Interaction is worth it. After getting an HCI PhD, you’ll have abundant career options available to you. You can secure top-paying human-computer interaction jobs like computer or information research scientist, UX researcher, or usability engineer roles.

If you decide to take the academic route after your PhD in HCI, you can teach at the top schools around the world after completing your degree. You can also conduct your own research, get funded, and get published as you teach. The options are endless for PhD degree holders.

Additional Reading About Human-Computer Interaction

[query_class_embed] https://careerkarma.com/blog/computer-scientists/ https://careerkarma.com/blog/computer-science-vs-computer-engineering/ https://careerkarma.com/blog/computer-science-research-topics/

PhD in Human-Computer Interaction FAQ

A PhD in HCI is usually four to six years long. However, there are part-time study options available that may take longer. During full-time studies, graduate students can finish their coursework in two or two and a half years and then take another year or two to complete their thesis.

The scope of human-computer interaction is very broad and covers multiple disciplines. With a human-computer interaction degree, you’ll acquire excellent skills and knowledge in various subjects, including computer science, cognitive science, mathematics, and technology, while also becoming qualified to work in these fields.

Yes, human-computer interaction can be quite tough. However, it can be less challenging to learn for students who have a natural aptitude for statistical, mathematical, cognitive, and technical subjects. It is doable for any student who develops a genuine interest and curiosity in the field.

There are many career options available to those with a PhD in Human-Computer Interaction. After getting a PhD in HCI, you can take the academic route and start teaching the subject while conducting your own research. You can also work in the field as an interaction designer, visual analyst, or UX researcher. Some students end up working with a university’s industry partners as well.

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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PhD Proposal: Speeding up Density Functional Theory with Machine Learning

IRB IRB-4105

The Kohn-Sham (KS) equations are a nonlinear eigenvalue problem of the form $H[\rho]\Psi = E\Psi$, where $H$ is a real symmetric matrix called the \textit{Hamiltonian}, $\Psi$ is an eigenvector called the \textit{wave function}, $E$ is an eigenvalue called the \textit{energy}, and $\rho(\mathbf{r}) = \sum_i |\Psi_i(\mathbf{r})|^2$ is an real-valued field called the \textit{charge density}, which is unknown a priori. The KS equations are nonlinear in the sense that the matrix $H$ depends on the charge density $\rho$, which in turn depends on the eigenvectors $\Psi$ of $H$. Typically it is solved by fixed-point iteration, where an initial guess for $\rho$ is made and then a sequence of \textit{linear} eigenvalue problems are solved until convergence. The cost of the iteration is dominated by the eigenvalue problem. Consequently methods of reducing the number of requisite iterations are of great interest.

In this thesis, we investigate the use of machine-learning models for solving the KS equations. Our strategy is to develop machine-learning models which approximate numerical quantities in existing algorithms for electronic structure. In particular we use equivariant graph-neural-networks to predict the Kohn-Sham charge density. We show such methods obtain an average savings of $13\%$ in the number of iterations needed to reach convergence. Our methods are general, but we focus on learning data from catalysis, an application with potentially large environmental impact.