Ph.D. in Neural Computation (PNC)
The Ph.D. Program in Neural Computation seeks to train new scientists in the field of neural computation.
Application Deadline
December 1
Related Programs
The Ph.D. Program in Neural Computation (PNC) is designed for students with strong quantitative backgrounds — such as in math, physics, computer science, or statistics and data science — who are interested in applying those skills to neuroscience research at Carnegie Mellon University or the University of Pittsburgh. Below, we outline how PNC differs from other related graduate programs at both universities.
The CPCB Ph.D. program differs substantially from the PNC in the foci of the biological and computational components of its curriculum. The CPCB program seeks to train students broadly in many areas of computational biology, with a focus on analysis at the molecular and gene levels. The CPCB program seeks to attract students with strong backgrounds both in biology and one quantitative area who want to do research in any field of computational biology.
The PNC, in contrast, is targeting students with a specific interest in neuroscience who have a strong quantitative background but may have little previous exposure to biology.
The PNC Ph.D. program differs from the Biological Sciences Ph.D. program in two key ways:
- Computational Focus: PNC requires training in computational methods, while the Biology Ph.D. program does not.
- Neuroscience Specialization: PNC focuses specifically on neuroscience, whereas the Biology program provides broad training across many areas of biology.
PNC is ideal for students with strong quantitative backgrounds who want to apply those skills to neuroscience research.
The graduate program at the Center for Neuroscience at the University of Pittsburgh trains students broadly in neuroscience. It is best suited for students with strong backgrounds in biological sciences or students from quantitative fields such as computer science, math, physics or statistics and data science, who are interested in a biologically focused neuroscience program.
In contrast, the PNC is designed for students who want to apply quantitative and theoretical methods to neuroscience research. PNC emphasizes computational approaches and is ideal for students with strong analytical skills who desire training in the application of quantitative and theoretical approaches to problems in neuroscience.
PNC/Machine Learning Joint Program
The program consists of the following core activities:
- The requirements for the Ph.D. in Machine Learning
- The four core course requirement of the Ph.D. in Neural Computation
- Exposure to experimental approaches through rotations or thesis research
- A roughly semester-long project to satisfy the PNC first-year research requirement and the first of the MLD speaking skills requirements
- A year-long project that would satisfy both the PNC second-year research requirement and the MLD Data Analysis Project requirements
- Training in teaching, scientific presentations and responsible conduct of research
- Successful defense of a Ph.D. thesis on a neuroscientific topic; if there is a single advisor, that person should be both a PNC training faculty member and affiliated with MLD; otherwise, the student may two co-advisors who, between them, have PNC and MLD affiliations.
In order to apply to a Joint ML PhD degree, a student must already be enrolled in one of the participating PhD programs in Machine Learning, Statistics, PNC, Heinz or SDS.
Before applying, a student must meet the following MLD requirements (in addition to any requirements from the other relevant Department):
- Take 10715, 36705, 10716 and earn at least a grade of A- in your first attempt to take each course. Letter grades are required. (Students who took courses before June 2023, will be Grandfathered in under the previous of B+ for the courses already taken.)
- Identify a MLD Core Faculty member who agrees to serve as their MLD mentor.
Applications must be submitted by May 31st.
The coursework is designed to ensure that students are well trained in neuroscience and that they also receive in-depth training in a set of quantitative approaches relevant to the field of neural computation. Students will complete the PNC and ML course requirements as outlined below.
A typical student will take 2-3 courses per term in their first year and complete all coursework by the end of their third year in the program. Because of differences in background and educational goals, course requirements for each student in the program will be adapted to their individual needs.
Neural Computation Course Requirements
Students must complete the four-core course requirement to gain graduate level training in the following four areas: cell and molecular neuroscience, systems neuroscience, cognitive neuroscience, and computational neuroscience. Courses fulfilling this requirement are:
- 03-762 Advanced Cellular Neuroscience
- 03-763 Systems Neuroscience
- 86-718 Brain Computation
- 86-765 Foundations of the Neural Basis of Cognition
Machine Learning Course Requirements
The MLD requirements for graduation with a Joint-ML PhD degree are the same as those for the regular MLD PhD requirements (including the requirement for the PhD thesis committee composition), with only the following differences:
- A Joint-ML PhD thesis will be a contribution to the combination of Machine Learning and the other field.
- The single elective course, the speaking and writing skills requirements, and the Data Analysis requirement (10718) may be satisfied within the student’s home department.
- A Joint-ML PhD student is still required to TA twice, but only one TA-ship has to be within MLD
Progress in the program is tracked based in part on students’ successful completion of program milestones. A committee selected by the student and approved by the program director evaluates the performance on milestones.
First year research requirement
By the end of the first calendar year in the program, all students are required to complete a computational project. This project will be evaluated by a committee consisting of at least three faculty, of whom at least two are PNC training faculty. The project requires the student to identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach. In some cases this project may be a precursor to the student’s eventual thesis project. This milestone will also count as the first of the MLD speaking skills requirements.
Second year research requirement
In the second year, students are expected to work on research about 1/3 of their time during the academic year and full time during the summer. By the end of the second full year in the program all students are required to complete a deeper computational project. The student’s work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication. This project will also count as the Data Analysis Project in MLD.
Ph.D. Thesis proposal
Required coursework should be completed by the end of the third year. During the fourth year a Ph.D. candidate should present a thesis proposal to his or her thesis committee and the community. The thesis proposal should include: a succinct summary of the proposed research problem; the significance of the proposed research; a review of relevant literature relating to the problem; a review of the candidate’s work leading up to the thesis, including preliminary results; a clear statement of remaining research; and a tentative schedule for completing the work.
Ph.D. Thesis Defense
Normally, the dissertation is completed during the student’s fifth year. The thesis defense is your chance to present the culmination of all your work and research to the neuroscience community and field questions from your thesis committee.
- All students will complete Responsible Conduct of Research Training.
- In order to build skills in teaching, mentoring, communication and management, each student will be required to serve as a teaching assistant for two courses during their career as a graduate student in the program.
PNC/Robotics Joint Program
The program consists of the following core activities:
- Coursework in computational neuroscience, quantitative methodologies, experimental neuroscience, and robotics
- Training in teaching, scientific presentations, and responsible conduct of research
- Successful defense of a Ph.D. Thesis
Applicants to the Joint PNC/Robotics PhD program must be enrolled in either the PNC or Robotics PhD program and meet the application criteria.
Applications must be submitted by May 31st.
Students must complete 48 units of course work. The course requirements are as follows:
- Students must pass coursework worth 12 units in a Foundations area, providing essential prerequisites for mastering the four core areas.
- Students must pass 36 units worth of coursework in the four core areas of Sensing and Perception, Thinking about Actions, Robot Embodiment, and Environment Interaction. Specifically, students must:
- take a course from each of the four core areas,
- pass two full semester courses each worth 12 units from two core areas that they wish to acquire deeper knowledge in, and
- pass two half-semester mini courses each worth 6 units from the remaining two core areas. Students can replace each mini course with a full semester course in the same core area with the understanding that their course load in the core areas then increases beyond the required 36 units.
| Foundations Area |
| 16-706: Foundations of Linear Algebra (6 units) |
| 16-707: Foundations of Optimization (6 units) |
| 16-708: Foundations of Statistics (6 units) |
| 16-709: Foundations of Software (6 units) |
| 16-710: Foundations of Manufacturing (6 units) |
| 16-811: Mathematical Fundamentals for Robotics (12 units) |
| Sensing and Perception Core Area |
| 16-720: Computer Vision (12 units) |
| 16-722: Sensing and Sensors (12 units) |
| 16-820: Advanced Computer Vision (12 units) |
| 16-822: Geometry-based Methods in Vision (12 units) |
| 16-823: Physics-based Methods in Vision (12 units) |
| 16-833: Robot Localization and Mapping (12 units) |
| 16-705: Fundamentals of Computer Vision Mini (6 units) |
| Thinking about Actions Core Area |
| 16-711: Kinematics, Dynamic Systems and Control (12 units) |
| 16-714: Advanced Control (12 units) |
| 16-745: Dynamic Optimization (12 units) |
| 16-782: Planning and Decision Making (12 units) |
| 16-831: Introduction to Robot Learning (12 units) |
| 10-701: Machine Learning (12 units) |
| 10-715: Advanced Machine Learning (12 units) |
| 15-780: Graduate Artificial Intelligence (12 units) |
| 16-702: Fundamentals of Learning Mini (6 units) |
| 16-703: Fundamentals of Control Mini (6 units) |
| Robot Embodiment Core Area |
| 16-778: Mechatronic Design (12 units) |
| 16-878: Advanced Mechatronic Design for Robotics (12 units) |
| 16-880: Engineering Haptic Interfaces (12 units) |
| 16-704: Fundamentals of Mechatronics Design for Robotics (6 units) |
| Environment Interaction Core Area |
| 16-741: Mechanics of Manipulation (12 units) |
| 16-761: Mobile Robots (12 units) |
| 16-867: Human Robot Interaction (12 units) |
| 16-700: Fundamentals of Manipulation Mini (6 units) |
| 16-701: Fundamentals of Human Robot Interaction Mini (6 units) |
As students in the joint program typically will be accepted to the joint program in their second year, they will be expected to complete the first-year milestones of their admitting program, without modification. In subsequent years, the milestones combine the requirements of the PNC and Robotics PhD programs. To be eligible for consideration for the joint program, the student must be in good academic standing in their admitting program, having successfully completed their year-one milestone in their admitting department.
PNC First year research requirement
By the end of the first calendar year in the PNC program, all students are required to complete a computational project. This project will be evaluated by a committee consisting of at least three faculty, two of whom are not one of the student’s advisors, and of whom at least two are PNC training faculty. The project requires the student to identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach. In some cases, this project may be a precursor to the student’s eventual thesis project. This project cannot substantially overlap with a project completed for a class, although it may be on the same topic as a class project, provided that it represents a substantial extension of that work.
Second year research requirement
In the second year, students are expected to work on research about 1/3 of their time during the academic year and full time during the summer. By the end of the second full year in the program all students are required to complete a deeper computational-robotics project. To fulfill the program research requirements, the student’s work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail, and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication. It is expected that this work will be written up as a manuscript suitable for submission to a journal in the relevant field; a draft of this manuscript must be submitted to the committee at least a week in advance of the meeting. In most cases this project will be in an area related to the student’s eventual thesis project.
With the addition of two TA semesters (1 PNC, 1 Robotics) this project will also fulfill the Robotics Research Qualifier.
Ph.D. Thesis proposal
Required coursework should be completed by the end of the third year. During the fourth year a Ph.D. candidate should present a thesis proposal to his or her thesis committee and the community. The thesis proposal contains both a written and oral component. Both components should include: a succinct summary of the proposed research problem; the significance of the proposed research; a review of relevant literature relating to the problem; a review of the candidate's work leading up to the thesis, including preliminary results; a clear statement of remaining research; and a tentative schedule for completing the work.
Ph.D. Thesis Defense
Normally, the dissertation is completed during the student’s fifth year. The thesis defense is your chance to present the culmination of all your work and research to the neuroscience and robotics community and field questions from your thesis committee.
- All students will complete Responsible Conduct of Research Training.
- In order to build skills in teaching, mentoring, communication and management, each student will be required to serve as a teaching assistant for two courses during their career as a graduate student in the program. Students pursuing the joint degree with Robotics will Additional Requirements
To apply for the Joint PNC/Robotics PhD program, students must be enrolled in either the PNC or Robotics PhD program and meet the following criteria:
PNC Students:
- Achieve a minimum 3.67 GPA in 36 units of specified RI PhD Core Courses.
- Identify a Robotics Core Faculty member as a mentor and meet with this mentor prior to submitting application.
Robotics Students:
- Identify a PNC Training Faculty member as a mentor and meet with this mentor prior to submitting application.
- Create a plan to complete remaining PNC degree requirements.
All applicants must submit the following to the Robotics and PNC PhD Program Administrators:
- Curriculum Vitae (CV)
- One-page Statement of Research Interests
- Unofficial CMU Transcripts
- Short recommendation letter from home PhD Advisor
- Short recommendation letter from secondary department mentor
- Requirement Completion Plan (Applicants to PNC only)
Applications must be submitted by May 31st.
PNC/Statistics Joint Program
The program consists of the following core activities:
- The requirements for the Ph.D. in Statistics
- The four core course requirement of the Ph.D. in Neural Computation
- Exposure to experimental approaches through rotations or thesis research
- A roughly semester-long project to satisfy the PNC first-year research requirement
- A year-long project that would satisfy both the PNC second-year research requirement and the Statistics Advanced Data Analysis preliminary exam requirements
- Training in teaching, scientific presentations and responsible conduct of research
- Successful defense of a Ph.D. thesis on a neuroscientific topic, with joint advisors, one from within Statistics and one from outside—both being CNBC-affiliated faculty members
Additional satellite activities through the CNBC will also foster students’ professional and scientific development.
Current PNC students looking to join the joint PNC/Statistics program may petition to do so by sending an email to the PNC co-directors explaining how they intend to satisfy the degree requirements.
The coursework is designed to ensure that students are well trained in neuroscience and that they also receive in-depth training in a set of quantitative approaches relevant to the field of neural computation.
A typical student will take 2-3 courses per term in their first year and complete all coursework by the end of their third year in the program. Because of differences in background and educational goals, course requirements for each student in the program will be adapted to their individual needs.
Core Course Requirement
Students must complete the four core course requirement to gain graduate level training in the following four areas: cell and molecular neuroscience, systems neuroscience, cognitive neuroscience, and computational neuroscience. Courses fulfilling this requirement, and a suggested order in which to take them include:
- 03-762 Advanced Cellular Neuroscience
- 03-763 Systems Neuroscience
- 86-718 Brain Computation
- 86-765 Cognitive Neuroscience
To meet the course requirements for the PhD in Statistics, students must take:
- 36-705: Intermediate Statistics (year 1)
- 36-707: Regression Analysis (year 1)
- 36-708: Statistical Machine Learning (year 1)
- 36-709: Advanced Statistics I (year 1)
- 36-750: Statistical Computing (year 1)
- 36-757: Advanced Data Analysis (year 1)
See Statistics Core PhD Requirements for details.
Progress in the program is tracked based in part on students’ successful completion of program milestones. A committee selected by the student and approved by the program director evaluates the performance on milestones.
First year research requirement
By the end of the first calendar year in the program, all students are required to complete a computational project. This project will be evaluated by a committee consisting of at least three faculty, of whom at least two are PNC training faculty. The project requires the student to identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach. In some cases this project may be a precursor to the student’s eventual thesis project.
Second year research requirement
In the second year, students are expected to work on research about 1/3 of their time during the academic year and full time during the summer. By the end of the second full year in the program all students are required to complete a deeper computational project. The student’s work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication. This project will also satisfy the Advanced Data Analysis project required by Statistics.
Statistics Preliminary Exams
By the end of the third full year the student should complete and pass the preliminary examinations for the Ph.D. in the Department of Statistics.
Ph.D. Thesis proposal
Required coursework should be completed by the end of the third year. During the fourth year a Ph.D. candidate should present a thesis proposal to his or her thesis committee and the community. The thesis proposal should include: a succinct summary of the proposed research problem; the significance of the proposed research; a review of relevant literature relating to the problem; a review of the candidate’s work leading up to the thesis, including preliminary results; a clear statement of remaining research; and a tentative schedule for completing the work.
Ph.D. Thesis Defense
Normally, the dissertation is completed during the student’s fifth year. The thesis defense is your chance to present the culmination of all your work and research to the neuroscience community and field questions from your thesis committee.
- All students will complete Responsible Conduct of Research Training.
- In order to build skills in teaching, mentoring, communication and management, each student will be required to serve as a teaching assistant for two courses during their career as a graduate student in the program.
CMU Rales Fellows
The Carnegie Mellon University Rales Fellows Program is dedicated to cultivating the next generation of STEM leaders and driving innovation by increasing access to a life-changing graduate education. By removing financial barriers to obtaining advanced degrees and providing Fellows with holistic support, the Rales Fellows Program empowers and connects scholars with others who share a passion for progress and innovation.