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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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. Current PhD students who will graduate by summer 2026 are encouraged to apply. Applicants with prior experience in mammalian cell culture, mouse models, or bioinformatics are preferred. People seeking dry
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scientists, and collaborators at academic and health-system sites nationally and internationally. The position is supported primarily through the DISCOVER-AI initiative, a three-year project within
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experimental 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
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. The successful candidate will hold a PhD by the start of appointment, be proficient in a range of biostatistical applications, and have had experience working with marginalized communities, especially people who
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with a primary home in the Department of Biostatistics. The postdoctoral associate will work with Dr. Bhramar Mukherjee, PhD , the inaugural Senior Associate Dean of Public Health Data Science and Data