The Michigan Institute for Data Science (MIDAS) is seeking applications for its Michigan Data Science Fellows (DS Fellows) Program. This two-year program provides outstanding young researchers with intensive data science experience as they ready themselves for independent research and faculty positions.  

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MIDAS is dedicated to building a data science research community and external partnerships, advancing innovative and responsible data science, and translating research outcomes to positive societal impact. The DS Fellows Program accepts young researchers who have a strong track record of research in their respective fields and who plan to acquire additional skills in data science, broadly construed, with a focus on either methodology or applications (including societal impact). Fellows will be expected to work at the boundaries between data science methods and domain sciences in an intellectually vibrant environment, and build interdisciplinary relationships with other Fellows and over 360 MIDAS affiliate faculty. They are expected to be strong candidates for university faculty and other research positions when they leave the program. 

Eligibility: Applicants must be outstanding, intellectually curious researchers who will be ready to pursue independent research positions after two years in the program.  They should have a doctorate in a related area before the expected start date of their fellowship. They should have never had a faculty position.  Collaboration experience and strong communication skills will be viewed favorably.  We recognize that strength comes through diversity and actively seek and welcome people with diverse backgrounds, experiences, and identities.  Individuals already at U-M are eligible to apply if their proposed project and faculty mentors are different from their current ones. 

Faculty Mentors:  Each Fellow must have one U-M faculty member as the primary mentor (the list of participating faculty will be posted by March 2). A second mentor with complementary expertise is encouraged, but not required. If needed, the primary mentor can help identify a suitable second mentor. Working together with MIDAS, the mentors will provide career guidance to the Fellows, meet with them regularly to assess research progress, help the Fellows seek opportunities for career advancement, and participate in activities that strengthen the Fellows community and the U-M data science community.

List of Faculty Mentors (Expand to View)

Name Email address Department or unit Current position
Carlos Aguilar caguilar@umich.edu Biomedical Engineering Assistant Professor
Veera Baladandayuthapani veerab@umich.edu Biostatistics Professor
Mihaela Banu mbanu@umich.edu Mechanical Engineering
Research Associate Professor
Christopher Brooks brooksch@umich.edu School of Information
Assistant Professor
Elizabeth Bruch ebruch@umich.edu Sociology & Complex Systems Associate Professor
Ceren Budak
cbudak@umich.edu School of Information Assistant Professor
Sriram Chandrasekaran csriram@umich.edu Biomedical Engineering Assistant Professor
Yang Chen ychenang@umich.edu Statistics Assistant professor
Lei Chen leichn@umich.edu UMD-Mechanical Engineering Assistant Professor
Walter Dempsey wdem@umich.edu Biostatistics Assistant Professor
Paramveer Dhillon
dhillonp@umich.edu School of Information Assistant Professor
Ivo Dinov dinov@umich.edu HBBS/SoN, DCMB/SoM Professor
Karthik Duraisamy kdur@umich.edu Aerospace Engineering Associate Professor
Marisa Eisenberg marisae@umich.edu Epidemiology and Complex Systems Associate Professor
Bogdan I. Epureanu epureanu@umich.edu Mechanical Engineering Professor
Johann Gagnon-Bartsch johanngb@umich.edu Statistics Assistant Professor
Lana Garmire lgarmire@med.umich.edu Depr of computational medicine and bioinformatics Associate professor
Eric Gilbert eegg@umich.edu School of Information Associate Professor
Libby Hemphill libbyh@umich.edu Information Associate Professor
Xun Huan xhuan@umich.edu Mechanical Engineering Assistant Professor
Abigail Jacobs azjacobs@umich.edu School of Information; Complex Systems Assistant professor
Jian Kang jiankang@umich.edu Biostatistics Professor
Evan Keller etkeller@umich.edu Urology, Michigan Medicine Professor
Andy Krumm aekrumm@umich.edu Medical School and School of Information Assistant Professor
Henry Liu henryliu@umich.edu Civil and Environmental Engineering Professor
Neda Masoud nmasoud@umich.edu Civil and Environmental Engineering Assistant Professor
Qiaozhu Mei qmei@umich.edu School of Information Professor
Inbal (Billie) Nahum-Shani inbal@umich.edu ISR
Research Associate Professor
Annette Ostling aostling@umich.edu Ecology and Evolutionary Biology Associate Professor
Jason Owen-Smith jdos@umich.edu ISR-SRC-IRIS Professor
Necmiye Ozay necmiye@umich.edu ECE Associate Professor
Shobita Parthasarathy shobita@umich.edu Ford School of Public Policy Professor
Huei Peng hpeng@umich.edu Mechanical Engineering Professor
Vitaliy Popov vipopov@umich.edu Medical School and School of Information Assistant Professor
Arvind Rao ukarvind@med.umich.edu DCMB (Comp. Med and Bioinformatics) Associate Professor
Jeffrey Regier regier@umich.edu Statistics Assistant Professor
Maureen Sartor sartorma@umich.edu Computational Medicine and Bioinformatics Associate Professor
Thomas Schmidt schmidti@umich.edu Internal Medicine Professor
Santiago Schnell schnells@umich.edu Department of Molecular & Integrative Physiology, Medical School
Department Chair and John A. Jacquez Collegiate Professor of Physiology
Sarita Schoenebeck yardi@umich.edu Information Associate Professor
Xu Shi shixu@umich.edu Biostatistics Assistant Professor
Misha Teplitskiy tepl@umich.edu School of Information Assistant professor
V.G.Vinod Vydiswaran vgvinodv@umich.edu Dept. of Learning Health Sciences Assistant Professor
Joshua Welch welchjd@umich.edu Computational Medicine and Bioinformatics Assistant Professor
Zhenke Wu zhenkewu@umich.edu Biostatistics Assistant Professor
Lei Ying leiying@umich.edu EECS Professor
Lili Zhao zhaolili@umich.edu Biostatistics
Research Associate Professor

Please contact these faculty members to determine interest before submitting the application.

Additional opportunity: The U. S. EPA National Vehicle & Fuel Emissions Laboratory is seeking a postdoctoral fellow in laboratory systems data analysis. Please apply to them directly. This Fellow will have the opportunity to work closely with U-M faculty and MIDAS Fellows.

Expectations:

  1. Salary: Comparable to norms in each research field, funded jointly by MIDAS and faculty mentors.
  2. Office space: The Fellows will be expected to spend at least 50% of time at MIDAS so that they can be part of the data science postdoc network and enhance the interdisciplinary reach of MIDAS.
  3. Professional service: The Fellows will be expected to spend 10% of their effort to be involved in MIDAS programs. Based on their career plans, they can choose to be involved in activities that develop resources for reproducible research, training and mentoring, and data science projects for positive societal impact. They will also be expected to interact with the U-M data science community through research presentations and other events.  

The Application Process:
Applicants may reach out to faculty members on the list to seek interest. The mentor(s) will need to be prepared to commit to 50% funding. The candidate and the mentor(s) will then submit the application to MIDAS. 

Applications will be accepted between March 2 and April. 23.

The Application Package: The application package should include:

  1. The candidate’s CV.
  2. A Research Statement, up to 4 pages, that includes: A. The proposed data science research, including its importance and potential impact; B. Qualifications that make the applicant particularly suitable for this program; C. The applicant’s career plan, how this Fellowship will benefit the plan, including a justification of why the applicant can be competitive as a faculty candidate within the short timeframe of this program.  
  3. A. reproducibility statement (1 page): MIDAS is dedicated to promoting reproducible data science.  Applicants should state how they will address the issue of the reproducibility of their research. This could be, but is not limited to, a rigorous method to reduce human errors in the study design or a thorough way to share data and code. A number of examples can be found on MIDAS website. It is important to articulate how their approach to reproducibility can be shared and adopted by other data scientists. Reproducibility may also be the focus of their research, such as the improvement of the robustness of data science methodology, or developing tools for reproducible research. 
  4. Each mentor should provide a Letter of Support, which includes the confirmation that the mentor is committed to providing 50% of the funding if the applicant is accepted to the program. The letter can be included in the application package, or emailed separately to midas-research@umich.edu by the April 23 deadline.  
  5. Names and contact information of two or three additional references.

Timeline:

  • Priority deadline: 11:59 pm, April 23, 2021. Applications received after this date will be considered only if spaces are still available.
  • Notifications will start on July 1., 2021.
  • The expected start date is Sept. 1, 2021, negotiable for special cases.

The University of Michigan is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, height, weight, or veteran status in employment, educational programs and activities, and admissions.

For questions about this application, please contact midas-research@umich.edu