Assistant Professor - Cellular, Molecular and Genetic Medicine

The Department of Cellular, Molecular and Genetic Medicine is seeking a term, research-track faculty member at the rank of Assistant Professor. We are looking for a candidate with expertise in quantitative genetics and bioinformatics to grow a research program who will develop independent, externally funded research programs that apply innovative state-of-the-art quantitative modeling approaches to important problems in medicine.

We are particularly interested in candidates whose research aligns with the following priority areas:

Developing biometrical models that incorporate data from twins and relatives, as well as genotype data to further our understanding of the etiologies of disorders, disease and traits.

Developing methods for the analysis of large-scale neuro-imaging, genomic and other high-dimensional data.

Developing approaches for causal inference from non-experimental data, such as data from relatives, instrumental variables or longitudinal studies.

Substantive applications of novel methods to address empirical questions concerning risk factors for mental and physical health outcomes. Examples from across the lifespan are of particular interest.

• →Research 100%: Develop rigorous, innovative, and externally funded research programs in Quantitative Genetics. Successful candidates will be expected to publish high-impact research, compete successfully for extramural funding, and build collaborative research programs to enhance the department's research portfolio.
• →Candidates will be expected to serve as highly collaborative researchers who can contribute to team science across CMGM, VIPBG, and the broader School of Medicine.

Minimum Qualifications

• A PhD or equivalent degree in quantitative, statistical, behavioral, or computational genetics/genomics
• Evidence of strong research productivity and potential to develop an independent, externally-funded research program
• Expertise in quantitative statistical modeling, or related computational methods relevant to medicine

Preferred Qualifications

The ideal candidate will have expertise in the following areas:

• Statistical Genetics: Experience with molecular genetics/genomics/epigenomics, population genetics, bioinformatics, multi-ancestry genomics, genetic variant curation, and statistical methods development.
• Classical Genetics: Proficiency in twin and family modeling within a structural equation modeling (SEM) framework (e.g., OpenMx). Experience with multivariate, longitudinal/developmental and causal modeling, instrumental variables, and working with both continuous and ordinal data is highly valued.
• Computational Genetics: Skills in clinical informatics, genomic data science, multi-omic analyses, biostatistical methods, machine learning, and AI. The ability to develop reproducible pipelines using high-performance computing and coding in Python, R, or other computational languages is a plus.
• Familiarity with the use of AI tools to assist coding and validity testing of novel methods.
• Experience with cloud computing, GPU-based processing, or large-scale biomedical data infrastructure.