Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship

Frequently Asked Questions:

Program Scope

What is AI?

The definition of AI for this program is broad: it includes machine learning, robotics, Bayesian inference, and simulation. However, this does not mean that everything in these topics is AI.  For example, servo-motor design may be important for robotics, but is not AI; and numerical methods for finite element analysis can be useful in many scenarios, but are not AI. On the other hand, our definition of AI is not limited to these four terms: it also includes many additional areas of expertise important for advancing AI in science and engineering that do not fit within these four buckets.  For example, data integration can be an essential precursor to the use of AI methods, and hence would be in scope. Similarly, automating research workflows is a topic of interest. 

What science and engineering domains are within scope?

Physical sciences, mathematical sciences, earth and environmental sciences, basic biological sciences, and engineering, except areas described in the following paragraphs. 

A note about computer science. Much of AI is in computer science, and the participation of AI methodologists is essential for the success of this program.  However, computer science is not a science or engineering domain within the scope of this program. 

New this year: We are now able to expand the scope of this program to include more biological sciences. Research that uses AI methods to understand the basic biological processes, disease mechanisms and propagation (in silico, in vitro and animals) is within the program scope.

Note: MIDAS has been, and continues to remain, a strong supporter of data science and AI in other disciplines, including biomedical, translational and healthcare research, and social sciences. However, any research that involves human subjects or focuses on disease prevention or treatment, including veterinary medicine, is out of scope of this particular program. Social science disciplines are also out of scope. We provide postdoctoral training in these disciplines through our sister program, the Michigan Data Science Fellows (DS Fellows) Program, which focuses on responsible data science and AI to improve their scientific and societal impact, understanding society through the use of new data types, and novel analytics for healthcare intervention. Potential applicants whose research is out of scope of the Schmidt AI in Science program may consider applying to the DS Fellows program.

Program Structure

What is the length of Fellows appointments? 

Appointments will begin between August 1 through September 15, 2024.  Appointments will be for a 12-month initial duration, and renewable in 12-month increments. Most appointments are expected to have a total duration of two years. The renewal for year 2 will be based on satisfactory performance in year 1. Additional renewals will be competitive and granted only in exceptional circumstances.

Is remote Fellowship allowed?

No. Fellows must work in-person on the University of Michigan Ann Arbor campus.

What does the program pay for? 

The program will pay a competitive salary ($78,000 annually for 2024-25) plus benefits. Travel to Schmidt Futures events will also be covered. In addition, there may be minor supplemental funding available for Fellows to attend AI training workshops / short courses and professional conferences.

What do the mentors pay for? 

The mentors cover all costs of the Fellows’ research, including research space, lab facilities, computers, computing resources, data access, travel to research conferences (at least two each year), publication fees, and other necessary costs*. Office desk space will be provided in MIDAS, but the faculty mentor may wish to make desk space available in their research group as well. The Science mentor (see below), by default, will be responsible to ensure the availability of all of the above; but the specific arrangements and shared responsibilities will be determined by the all mentors of each Fellow. 

*For questions or concerns regarding the financial support of visa fees, mentors should contact the Program Team,

Who may qualify to be a faculty mentor? 

A faculty mentor can be any faculty member on the tenure track, clinical track, or research track with a full-time appointment at the University of Michigan in any of the Ann Arbor, Flint, or Dearborn campuses, who is a MIDAS affiliate or has applied to be one.  

Faculty members at other universities may not be named mentors. However, they may be members of the Fellowship Committee if they are able to fulfill the obligations of the committee. They may also serve as additional mentors.

For the sake of the program’s scientific diversity, faculty mentors of the current Fellows are discouraged to be mentors of new applicants. 

What is the role of faculty mentors?

Each fellow is expected to have two named mentors, a Science mentor and an AI mentor.  Together, they will take responsibility for defining the research challenge, ensuring that the Fellow has adequate domain knowledge to tackle this challenge, and ensuring that the Fellow has adequate AI skills to execute this approach. How the two mentors jointly fulfill their duties, for example, whether to designate a primary mentor, how exactly to divide responsibilities, and who covers which portion of the research costs, will be decided between the mentors. MIDAS will require letters of support from both mentors to expressly commit to their responsibilities. Additional mentors are allowed but not required.

Who should be a Science mentor and who should be an AI mentor?

What we really look for is strong mentoring for both the research direction (and the specific project) and AI skills training. Oftentimes one mentor has stronger AI expertise, and the other has stronger domain expertise. In such cases it’s easy to designate the two mentors’ roles. However, sometimes both mentors are strong in AI and in domain science. In these cases we let the mentors make the designations with the understanding that, with their combined expertise, they will provide sufficient mentoring in both research and AI training to the Fellow. In the application, this should be clearly explained; Fellow and mentor applicant teams will be reviewed more favorably if the plan for AI and science domain mentoring is clearly explained and justified.  

What is the Fellowship Committee?

After a Fellow starts in the program, they and their mentors, program directors, and one additional faculty member (preferably a close collaborator at U-M or elsewhere) will form the Fellowship Committee. The committee will meet every six months to review progress in research and training, and provide guidance to the Fellow. The Fellow can revise their Individualized Development Plan based on the committee’s recommendations.

How does the program promote diversity, equity and inclusion? 

Diversity, equity and inclusion (DEI) is a core value at U-M, at MIDAS, and in this postdoc program.  The program values diversity in both the Fellows and the faculty mentors. The program seeks demographic diversity across race, gender, ethnicity, personal background and beliefs; professional diversity such as the PhD granting institutions of the Fellows and their career directions. The program also seeks to support Fellows working in a diversity of fields in science and engineering on a wide range of research questions.  

The program will be advertised through multiple channels to reach diverse applicants. During the application process, the program team will help applicants connect and communicate with potential faculty mentors, to ensure equity among applicants from all backgrounds. Reviewers of applications will be required to follow anti-bias best practices developed by the university. The Fellowship selection committee members are expected to have gone through anti-bias training. In addition, all candidates are required to submit a DEI statement. The program will also develop a rubric to help faculty mentors mitigate unconscious bias when they make subjective judgments.

After the Fellows join the program, we will effectively use the Individualized Development Plan and the Fellowship Committee to ensure equity in their training and career preparation. We will promote inclusion strongly in collaborative learning and community building activities.

Will the fellowship involve training? 

A key aspect of the program is skill development for the Fellows.  The program has many components to facilitate the Fellows’ skill development. However, such skill development will be effective only if Fellows are motivated. Furthermore, passive reliance on program support is unlikely to suffice. As such, we explicitly require an Individualized Development Plan for each Fellow, which will be crafted and revised by the Fellows, their mentors and their Fellowship Committees. We will also track the progress of the Fellows closely.

What are the program’s collaborative learning activities?

The program has the following built-in activities.

Onboarding bootcamp. The program organizes a weeklong boot camp for each new cohort of Fellows, along with continuing Fellows and mentors (mentors may attend only a portion). The boot camp will consist of AI tutorials tailored to the Fellows’ research needs, and research presentations by the Fellows, mentors and other potential faculty collaborators.

“AI carpentries”. The Fellows form “AI carpentries” – small groups based on research directions and AI skills. Members of each carpentry will be required to work together at MIDAS collaboration space one day a week. Faculty mentors will take turns attending and guiding the work.  

Weekly research meetings. All Fellows attend these meetings to discuss projects, pitch new ideas, share interesting papers, practice their presentations, and carry out other group learning activities. Other postdocs and faculty members on campus will also be invited to these meetings to pitch research ideas and build collaboration.

AI in Science workshops. Each semester, the Fellows will be required to develop a one-day workshop for the campus, with the supervision of their mentors. The workshops may include AI tutorials and present case studies on how AI methods can be applied to science and engineering research. The Fellows will solidify their AI skills through teaching.


What are the expectations for the Fellows?

First and foremost, the Fellows should be committed to successfully implementing their research plan and the Individual Development plan. But equally importantly, they should be committed to being active members of the postdoc program and the U-M AI in Science research community.

Each Fellow will have a desk in MIDAS space, and is expected to spend at least 50% time in this space. Each Fellow should also spend an appropriate amount of time with their faculty mentors’ research groups (based on agreements between the Fellows and mentors).

After onboarding, each Fellow is required to set up a Fellowship Committee as described above.

All Fellows are required to actively participate in the collaborative learning activities (see the “Program Structure” section). 

All Fellows are also expected to participate in programmatic activities that can help build their professional service experience. They may choose to participate in training activities as instructors, or organize research connection events, or develop algorithms, tools, research protocols that can be shared with the campus research community. These activities should account for, on average, 5% of the Fellows’ effort.

The Schmidt AI in Science program will organize events across the postdoc programs that it funds at multiple universities. Fellows should abide by their attendance requirements.

After they leave the program, Fellows are expected to provide their CV to program management at MIDAS annually for at least five years, as well as an annual summary of their research activities and accomplishments, and how they advance the application of AI to enable research breakthroughs. 

What are the obligations for the faculty mentors?

Mentors are expected to devote sufficient time to provide research and career guidance to the Fellows, meet with them regularly, attend Fellowship Committee meetings every six months, and help Fellows seek opportunities for career advancement. They are also required to cover all costs of the Fellows’ research, other than Fellow salary and benefits, as discussed above in “What do the mentors pay for?” and “What is the role of faculty mentors?”. 

In addition, mentors are expected to actively participate in program activities, including developing training curriculum, supervising the Fellows to develop workshops, and attending collaborative learning events. A rough estimate of time commitment for some of the events is as follows:

  • Annual bootcamp: each mentor is expected to attend parts of this weeklong event.
  • AI Carpentries: each mentor is expected to join these group working sessions (working at MIDAS space) for 2-5 days a year.
  • Weekly research meetings: occasional attendance.
  • Fellow selection: Several hours of application review once a year.
  • Participating in the AI in Science curriculum committee: Several hours a year.
  • Any attendance requirement set up by the funder (for example, they may invite some faculty mentors to their annual conference).

Qualifications and the Application Process

What are expected candidate qualifications?

The candidate must have a PhD degree prior to the start date of the Fellow appointment, but within the past five years.  The PhD can be in any field. (Note the science and engineering domains listed above that are in scope: these apply to the area of proposed work, but not to the field of PhD degree). It is acceptable for the candidate to already have a postdoc position, whether at U-M or elsewhere (but please see below about U-M candidates). However, the candidate should not have had a faculty appointment anywhere that is equivalent to a North American university’s tenure-track position.

The ideal candidate will have training in both AI (at least one sub-area) and the domain discipline.  However, this may not be the case for many candidates.  We require that a candidate have deep expertise in at least one of the two: either AI methodologies or the domain discipline, in conjunction with demonstrated interest in the other.  The key requirement is evidence of excellence and a passion for the proposed research direction at the intersection of two disciplines.

How and when do candidates apply?  

See the program website for information about how and when to apply.  For the 2024 cohort, the application will open on September 1, 2023 and the submission deadline is November 30, 2023.  

How firm is the start date? 

To develop a true Fellows cohort each year, and to allow synchronized programmatic activities and skill building for each cohort, the program has a target window of start dates between Aug. 1 and Sept. 15, 2024.  Some exceptions may be possible, in special circumstances, and will need to be determined case by case.

I have already defended my thesis, but my PhD degree will not be conferred until a later time. Can I apply?

If you have already successfully defended your thesis, but your degree has not been officially conferred, you are eligible to apply as long as the conferral will happen before or shortly after your start date. You will need an official letter from the appropriate University authority stating that you have completed all requirements and your degree will be conferred on such and such date.

Is the program open to international applicants? 

Yes, the program is open to international applicants.  There is no citizenship requirement for the applicants.  Accepted Fellows will need to obtain the appropriate visa before joining the program. If for any reason a visa is not granted, the acceptance will be voided.

Is the program open to U-M internal candidates?

Yes, but only if they propose research projects that are significantly different from their current projects, AND if they will work with a different set of mentors from their current ones. In other words, we seek new research directions from U-M applicants as opposed to research that continues a previous project or continues with previous mentors. 

How should candidates find potential faculty mentors?  

The candidate should seek at least one faculty mentor either in AI methodology or in an application domain. If one faculty member agrees to be a mentor, they may work with the candidate to find a second mentor. Candidates may find it useful to use the MIDAS website to search through the 500 affiliated faculty, although faculty mentors need not be selected from this list. Also, a list of faculty members who have expressed interest in being AI mentors is available on the Schmidt AI in Science program website in the people section.

However, candidates should feel free to reach out to faculty members not on this list.

If needed, the program team will also help candidates identify mentors.

Can the mentors help the candidate to prepare the application package?

Yes. Just like mentors can help their trainees prepare an application for an NIH or NSF funded research fellowship, or a fellowship funded through a corporation, mentors can and are encouraged to help a candidate prepare the application to our Fellowship program. Specifically, we encourage mentors to discuss extensively with the candidate both the proposed research project and the Individualized Development Plan. 

Can a faculty member serve as a mentor to more than one applicant?

Yes. We will not dismiss or downgrade an application on account of a mentor who expresses support for multiple fellow applicants.  So it is OK to support multiple applicants.

However, keep in mind that we seek diversity along many dimensions.  Having two Fellows working with the same mentor goes against that, and so it is unlikely we will want that in our final portfolio. For the same reason, faculty mentors of the current Fellows are also discouraged to be mentors of new applicants.

Our advice to prospective mentors of more than one applicant: if you think one applicant is clearly stronger than others, go only with this one.  However,  if you cannot decide between two or more applicants, please feel free to support all and let us evaluate. 

How well should the proposed research project be specified in the application?

We understand that the nature of research is that there are many unknowns. However, the research statement is one of the most important selection criteria. The research statement should include a compelling vision and a plausible approach to achieving that vision. Note that the program seeks to promote research that is transformative and creative, with the potential to spur significant advances in the application of AI in science domains.  

How comprehensive should the Individualized Development Plan (IDP) be in the application?

No need to be comprehensive. The IDP in the Fellowship application should demonstrate that the applicant and their mentors are aware of the applicant’s training needs and are committed to meeting such needs. We also do not favor one type of training over another. For example, we will not require Fellows to take formal classes. The IDP should be effective and feasible for each Fellow. The key evaluation criterion here is whether training needs have been adequately thought through and planned for.

A set of AI competencies and training resources will be developed soon and made available to enrolled Fellows and applicants. 

After a Fellow starts in the program, they will work with the mentors and the Fellowship Committee to revise the plan multiple times based on their training needs and progress.

Why do I need a responsible AI statement when I don’t work with human data?

Responsible AI includes not only scientists’ responsibility to society, but also their responsibilities to science. From the rigor of study design and the explainability of models, to the unbiased use of data and algorithms, and to data sharing and making a research project fully reproducible, responsible AI is critical to all applicants, because all researchers need to take into consideration the effect of flawed data, brittle analyses, and irreproducible results.

What do you look for in the DEI statement?

We look for sincere and feasible ideas that contribute, even in small ways, to the diversity, equity and inclusion of the postdoc program, and to the AI in Science and Engineering research community on campus.  A candidate’s life experiences can be described where they are relevant to DEI and a candidate’s past activities can be used to illustrate their contributions to promoting DEI.  

Should the mentors’ letters of support be included in the application package, or sent separately to MIDAS?

Yes. These letters are similar to letters of support in training grants, and should be included in the application package. These are different from reference letters, which are usually confidential.

How do I decide whether to request two or three reference letters?

If you can secure three reference letters that can provide valuable information to the reviewers, you should. Having strong letters that can speak to your strengths and achievements will help you. However, we only require two letters. This can work well if one of your proposed mentors already knows you well, and can speak about both your future and your past in the letter of support.

Other Questions?

For questions, please contact