Please submit a CV, a cover letter describing your research experience, your scientific interests, and your specific reasons for applying to this lab, and contact information for three references. Cover letters that engage with the lab?s published work and articulate a research vision will receive strongest consideration. Review of applications will begin immediately and continue until the position is filled.

The Parker Lab generates mechanistic knowledge about how disease susceptibility is encoded in the non-coding genome, with a focus on metabolic traits. We combine statistical genetics, single-cell and single-nucleus multi-omics, and computational genomics to translate GWAS signals into biology. Our work spans method development, data generation (snRNA-seq, snATAC-seq, MPRA, spatial omics), and integrative analysis at scale, and we work closely with collaborators across the diabetes research community. Dr. Parker also directs the Epigenomic Metabolic Medicine Center (EM?C) at the University of Michigan.

We are a group of roughly 20 scientists, and we take lab culture seriously. We value openness, scientific rigor, direct and collegial communication, and good humor. We invest in each other through regular mentorship, quarterly wellness meetings, and an annual lab retreat. For more information, see http://theparkerlab.org.

Researchers from traditionally underrepresented groups are highly encouraged to apply. We value inclusivity.

Michigan Medicine improves the health of patients, populations and communities through excellence in education, patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.  Our mission is guided by our Strategic Principles and has three critical components; patient care, education and research that together enhance our contribution to society.

Postdoctoral Fellow in Statistical Genetics and Regulatory Genomics

The Parker Lab at the University of Michigan (http://theparkerlab.org) is recruiting a Postdoctoral Fellow to lead statistical genetics research on type 2 diabetes and cardiometabolic disease. We are a computationally focused lab in the Departments of Computational Medicine & Bioinformatics, Human Genetics, and Biostatistics, working at the interface of GWAS, single-cell multi-omics, and tissue biology.

The successful candidate will own a research program centered on connecting non-coding disease variants to their cell- and context-specific regulatory mechanisms. Core activities include cell-type-specific e/caQTL mapping in single-nucleus multi-omic data (snRNA-seq and snATAC-seq), colocalization and fine-mapping against T2D and related trait GWAS, Mendelian randomization, heritability partitioning (for example, LDSC and related approaches), and regulatory network inference. Tissues of focus include pancreatic islets and skeletal muscle, with opportunities to extend to other metabolic tissues through active collaborations. The position has scientific latitude: we expect the postdoc to drive analytical strategy, make and defend methods choices, interpret results critically, and shape the direction of their projects in partnership with team.

The successful candidate will also help mentor graduate students, rotation students, and other trainees, and contribute to grant writing and manuscript preparation. This position is well suited for applicants who see a postdoc as the launching point for an independent academic research program.

• PhD (completed or imminent) in Biostatistics, Statistical Genetics, Computational Biology, Bioinformatics, Human Genetics, Computer Science, or a closely related quantitative field.

• Demonstrated depth in statistical genetics methods, such as GWAS analysis, fine-mapping (for example, SuSiE), colocalization, Mendelian randomization, heritability estimation, or QTL mapping. We prioritize methodological understanding over tool familiarity.

• Strong programming skills (R and/or Python) and comfort in a high-performance computing environment.

• Evidence of analytical independence: the ability to define a question, choose appropriate methods, interpret results skeptically, troubleshoot unexpected findings, and revise course.

• A publication record appropriate to career stage, including at least one first-author paper or preprint.

• Excellent written and oral communication, including being both collaborative and coachable.

• English language proficiency.

• Experience with single-cell or single-nucleus multi-omic data (snRNA-seq, snATAC-seq, multiome).

• Familiarity with regulatory genomics, chromatin data, or regulatory network inference.

• Prior work on metabolic, endocrine, or cardiometabolic disease.

We welcome applicants with strong statistical genetics foundations who are ready to extend into single-cell data, and we also consider applicants with single-cell expertise who have a clear plan to develop statistical genetics depth.

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Job openings are posted for a minimum of seven calendar days.  The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

The University of Michigan is an equal employment opportunity employer.