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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar
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, comparative genomics, machine learning, and evolutionary analysis to address fundamental questions in molecular biology and human disease. Responsibilities Develop computational pipelines for the discovery
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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biosafety level 2 (BSL2+) lab), immunology (such as flow cytometry and CAR-T engineering), molecular biology (such as CRISPR, RNA biology), or animal models, (e) collaboration with other researchers both
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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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platforms a plus Experience applying machine learning, artificial intelligence, and large language models to research a plus The anticipated start date is September 1, 2026. The postdoctoral position incoming