Post Doctoral Scholar - Henn Lab

University of California, Davis; Davis (CA); On-site; USD 60,000 - 90,000; Full time

Job summary

The Henn Laboratory at University of California, Davis is seeking a Postdoctoral Fellow to advance two integrated research programs: GWAS methods for underrepresented African populations and adaptive evolution at HLA/MHC loci in admixed populations. Collaboration with Brown University and McGill will support the SWIF(r) toolkit for selection inference.

The position involves developing pipelines, demographic models, and HLA simulations (msprime/SLiM), and leading manuscript preparation and

Qualifications

• PhD in evolutionary biology or related field.
• Experience with population genetic methods and large-scale genomic datasets (WGS, SNP arrays).
• Proficiency in Python and/or R; comfort in Unix/Linux computing environments.
• Peer-reviewed publication(s) or manuscripts in preparation.

Responsibilities

• Build and maintain QC and variant-calling pipelines for next-generation genomic sequencing data.
• Develop population-specific demographic models and encode parameters in stdpopsim format.
• Train SWIF(r) per population and selection regime; integrate HLA summary statistics; compute posterior probability distributions.
• Develop and validate HLA simulation framework (msprime/SLiM) for directional and balancing selection; release via GitHub.
• Execute selection-guided GWAS for BMI, tuberculosis susceptibility, and skin pigmentation.
• Lead peer-reviewed manuscript preparation and conference presentations.

Skills

Python

R

Unix/Linux

Education

PhD in evolutionary biology or related field

Tools

SWIF(r) toolkit

msprime

SLiM

GitHub

Job description

The Henn Laboratory is seeking a Postdoctoral Fellow to advance two integrated research programs: (1) selection-guided GWAS methods applied to underrepresented African populations, leveraging the Northern Cape Tuberculosis Cohort, and (2) inference of adaptive evolution at the HLA/MHC loci in admixed populations. The position involves active collaborations with Brown University and McGill University on the SWIF(r) machine learning toolkit for selection inference.

Key Responsibilities

• Build and maintain QC and variant-calling pipelines for next-generation genomic sequencing data
• Develop population-specific demographic models and encode parameters in stdpopsim format
• Train SWIF(r) per population and selection regime; integrate HLA summary statistics; compute posterior probability distributions
• Develop and validate HLA simulation framework (msprime/SLiM) for directional and balancing selection; release via GitHub
• Execute selection-guided GWAS for BMI, tuberculosis susceptibility, and skin pigmentation
• Lead peer-reviewed manuscript preparation and conference presentations

• PhD in evolutionary biology, statistical/human genetics, computational biology, or related field
• Experience with population genetic methods and large-scale genomic datasets (WGS, SNP arrays)
• Proficiency in Python and/or R; comfort in Unix/Linux computing environments
• Peer-reviewed publication(s) or manuscripts in preparation