Job Description

Job Summary
The Terhorst Lab in the Department of Statistics at the University of Michigan is recruiting a postdoctoral research fellow in statistical genetics and computational genomics. The position is part of a collaborative project with the University of Edinburgh and the University of Oxford focused on developing scalable methods for complex trait analysis using ancestral recombination graphs (ARGs).

Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed models, and efficient algorithms for large-scale genomic data analysis.

This is a one-year appointment starting as early as possible, with renewal possible based on the availability of funds, availability of work, and satisfactory performance.

Responsibilities*
Develop novel statistical and computational methods for ARG-based quantitative genetics
Analyze large-scale genetic and phenotypic datasets
Implement scalable software and algorithms for genomic inference
Collaborate with researchers across statistics, genetics, and computational biology
Contribute to manuscripts, presentations, and open-source software development
Participate in interdisciplinary collaborations related to predictive breeding and genome editing
Travel to the United Kingdom to collaborate with project partners at the University of Edinburgh and the University of Oxford
Required Qualifications*
PhD in statistics, computer science, computational biology, genetics, applied mathematics, or a related quantitative field is required
Proof of degree completion must be in hand on or before the start date
Strong programming and computational skills
Experience with statistical modeling, machine learning, or large-scale data analysis
Desired Qualifications*
Experience with population genetics or statistical genetics
Familiarity with Bayesian methods, probabilistic modeling, or graphical models
Experience with scientific computing in Python, JAX, Torch, Julia, C++, or related languages
Experience with high-performance computing or scalable algorithms
Interest in interdisciplinary research spanning genomics and evolutionary biology