The Chen Lab at the University of Chicago is seeking outstanding researchers to contribute to the development and application of advanced statistical and AI/ML methods for analyzing large-scale genomics data as part of a large NIH-funded international consortium that brings together multiple institutions to analyze genomics data from human cohorts diagnosed with epilepsy.
Requirements
- Experience in either: Large-scale genomics data analysis OR Statistical/AI/ML method development applied to biological data.
- Proficiency in programming languages and working with high-performance or cloud computing environments.
- 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.
- Solid understanding of genetics principles and genetic association studies.
Responsibilities
- 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.
- Serves as a resource for collecting data and performing analysis.
- Contributes to facilitating and promoting a research project by providing scientific or intellectual information.
- Performs other related work as needed.
Other
- Discuss, plan, and carry out research in a stimulating and collaborative environment.
- Contribute to the supervision and mentoring of junior researchers.
- Collaborate with consortium partners, with excellent opportunities to expand academic networks.
- Trains new laboratory personnel.
- Demonstrated scientific writing skills in English, evident from first-author journal articles.
- Proactive mindset with strong communication skills to work effectively in an interdisciplinary team.
- Enthusiasm for initiating innovative secondary analyses, such as integration with multimodal functional and clinical data from external resources.
