Summary

The Atkinson Lab in the Department of Molecular and Human Genetics at Baylor College of Medicine is recruiting a motivated Postdoctoral Associate to lead research projects in statistical and population genomics. The postdoctoral associate will use large-scale genomic datasets to develop, evaluate, and apply ancestry-informed methods for studying complex traits across human populations. The position offers opportunities to drive analyses within major consortium projects, contribute to statistical and computational method development, publish high-impact research, and gain mentorship experience through training junior members of the group.

The Postdoctoral Associate will have flexibility to tailor projects to their own related interests and to contribute to a range of ongoing efforts in the lab, that includes:

• Elucidating the genetic architecture of complex traits, with an emphasis on psychiatric phenotypes, using ancestry-informed approaches for GWAS, polygenic risk scoring, fine-mapping, and heritability analyses.
• Extending and developing statistical and computational tools for genomic analyses in ancestrally heterogeneous and admixed cohorts.
• Characterizing patterns of admixture, demographic history, and natural selection relevant to human genetic variation and complex trait architecture.
• Building scalable analytic pipelines for large biobank, consortium, and population genomic datasets.

The lab has leadership roles in multiple international consortia, that includes the Psychiatric Genomics Consortium PTSD Working Group, the Neuropsychiatric Genetics in African Populations study, and the Latin American Genomics Consortium. These collaborations provide access to a wide range of large-scale genomic datasets, phenotypes, and opportunities for collaborative research. Baylor College of Medicine is a global leader in genetics research and provides a vibrant multidisciplinary environment with close links to institutions across the Texas Medical Center and BCM-affiliated hospitals throughout Houston.
As a member of the Atkinson Lab, the Postdoctoral Associate will contribute to collaborative research spanning population genetics, statistical genetics, psychiatric genetics, and complex trait genomics. While projects may involve a range of phenotypes and populations, the lab has particular strengths in psychiatric traits, admixed populations, local ancestry inference, GWAS, polygenic prediction, and scalable genomic methods.

Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.

Job Duties

• Designs, implements, analyzes, and interprets statistical and computational genomics studies.
• Leads analyses of large-scale genomic datasets, including cohorts with complex population structure and/or admixture.
• Develops, evaluates, and applies ancestry-informed statistical genetics methods.
• Implements GWAS, PRS, heritability, fine-mapping, demographic inference, and related analyses.
• Builds flexible analytic pipelines and computational resources for large-scale genomic studies.
• Conducts analyses using high-performance computing clusters and/or cloud-based systems.
• Integrates genomic and, where relevant, multi-omic or phenotypic data to enhance complex trait analyses.
• Characterizes the genetic architecture of psychiatric and other complex disorders.
• Writes manuscripts, abstracts, and conference presentations.
• Presents findings at lab meetings, conferences, and consortium working groups.
• Contributes to collaborative projects with internal and external investigators.
• Assists with training and mentoring junior researchers in computational and statistical genomics.
• Performs other job-related duties as assigned.

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• PhD in genomics, population genetics, statistical genetics, psychiatric genetics, bioinformatics, statistics, computational biology, or a related field.
• Experience in human population genetics, complex trait genomics, and/or statistical genetics.
• Experience working with large-scale genomic datasets.
• Experience with GWAS, PRS, heritability estimation, fine-mapping, local ancestry inference, demographic inference, or related methods.
• Experience with psychiatric genetics, admixed populations, or biobank-scale analyses is a plus.
• Coding experience, preferably in Python, R, Bash, or related languages.
• Familiarity with high-performance computing clusters, cloud-based platforms, or reproducible workflow tools.
• Comfort with statistical analysis, including linear and logistic regression.
• Strong written and oral communication skills.
• Interest in collaborative research and mentorship.
• Scientific creativity, independence, and the ability to work well as part of a team.