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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and
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learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based
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biennial CMB retreat; Support and report on initiatives from our internal communities, including the Postdoc committee, PhD committee, Tech committee, and Eco teams; Maintain the Centers’ relationships with
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Willingness to combine wet-lab experimental work and computational analysis Basic knowledge of statistics and/or programming (R, Python, or similar) Strong motivation to learn new techniques across
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Willingness to combine wet-lab experimental work and computational analysis Basic knowledge of statistics and/or programming (R, Python, or similar) Strong motivation to learn new techniques across
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acquire their fate and establish precise connectivity with target brain regions during development, and how these processes are altered by stress and pathology. To address these questions, the team combines