Job Description

We have multiple vacancies for excellent research scientists and assistants to contribute to the development and application of advanced statistical and AI/ML methods for analyzing large-scale genomics data. Will you join our project?

The Team

The newly established Chen Lab ( is based in the Department of Human Genetics at the University of Chicago, affiliated with UChicago Medicine and the Broad Institute of MIT and Harvard. Our research strives to catalyze repeated traversal of the 'genomic medicine cycle,' driving the discovery, biological understanding, and clinical translation of the genetic underpinnings of human disease. Our lab plays a leading role in multiple international consortia, including Epi25, the International League Against Epilepsy (ILAE), and the Genome Aggregation Database (gnomAD). Leveraging advances in genomics technologies, we have made seminal discoveries that elucidate the genetic basis underlying conditions ranging from severe neurodevelopmental disease to population-level phenotypic variation. Our work has been published in high-profile journals including Nature, Nature Genetics, Nature Neuroscience, and others. We are currently expanding efforts to build large-scale data commons for human complex disorders and to integrate emerging technologies such as AI to drive the next wave of genomic and biomedical discovery.

The Candidate

This project is part of Epi25, a large NIH-funded international consortium that brings together multiple institutions to analyze genomics data from human cohorts diagnosed with epilepsy. Epi25 represents the world’s largest epilepsy genetics initiative, and in collaboration with clinical laboratories we are substantially expanding to include cohorts with diverse neurodevelopmental disorders (NDDs). The overarching goal is to better understand the genetic basis of these disorders and translate discoveries into novel therapeutic strategies.

As a team member, you will develop and apply data-driven statistical and AI/ML techniques to discover genes and mutations driving disease risk and reveal biological processes across the spectrum of NDD phenotypes. You will also build tools and pipelines that enable the broader research community to leverage innovations generated in these projects. Through Epi25, you will gain access to the largest epilepsy and NDD cohorts worldwide, encompassing tens of thousands of samples with exome sequencing and SNP-array data, organized in case-control, trios, or family-based studies. You will be able to lead your own project and enjoy collaboration opportunities with an international, interdisciplinary team of leading researchers and clinicians.

Key Responsibilities:

• Discuss, plan, and carry out research in a stimulating and collaborative environment.
• Develop and apply rigorous methods to investigate associations between genetic and phenotypic variables.
• Independently explore, learn, and evaluate state-of-the-art statistical/AI/ML approaches.
• Summarize findings in reports, manuscripts, and presentations; publish results in peer-reviewed journals.
• Conduct reproducible analyses within an open science framework, including producing open-source pipelines and tools for use by the broader scientific community.
• Contribute to the supervision and mentoring of junior researchers.
• Collaborate with consortium partners, with excellent opportunities to expand academic networks.

Qualifications

Essential:

• Bachelor’s degree or higher in computational biology, bioinformatics, statistics, computer science, AI/ML, or a related quantitative discipline; a PhD is required for scientist positions.
• Solid understanding of genetics principles and genetic association studies.
• Experience in either:
• Large-scale genomics data analysis.
• Statistical/AI/ML method development applied to biological data.
• Proficiency in programming languages and working with high-performance or cloud computing environments.
• Proactive mindset with strong communication skills to work effectively in an interdisciplinary team.

Desirable:

• Practical experience with sequencing or SNP-array data analysis.
• Experience with genomics data analysis tools and frameworks (e.g., PLINK, Hail, GATK).
• Strong knowledge of modern statistical/AI/ML methodologies in biology.
• Enthusiasm for initiating innovative secondary analyses, such as integration with multimodal functional and clinical data from external resources.

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