Research Fellow

Postdoctoral Fellow in Statistical Genetics and Regulatory Genomics

The Parker Lab at the University of Michigan 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.