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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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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models
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start from the observation that combining a weak endocytic mutant with an autophagy mutant causes a strong developmental delay under non-stressed conditions. The research will involve interaction
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We are seeking a motivated and creative PhD student to develop the next generation of AI-driven protein design methods that explicitly account for protein–lipid and protein–membrane interactions
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We are seeking a motivated and creative PhD student to develop the next generation of AI-driven protein design methods that explicitly account for protein–lipid and protein–membrane interactions
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environments. Interest in antibody/nanobody discovery and protein–protein interactions. Experience with structural bioinformatics or AI‑based modeling is a plus. Our offer A competitive salary and
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environments. Interest in antibody/nanobody discovery and protein–protein interactions. Experience with structural bioinformatics or AI‑based modeling is a plus. Our offer A competitive salary and
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Chlamydomonas reinhardtii. The project will investigate how genome dynamics and environmental conditions interact to shape the evolutionary success of polyploids using: Experimental evolution approaches
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Chlamydomonas reinhardtii. The project will investigate how genome dynamics and environmental conditions interact to shape the evolutionary success of polyploids using: Experimental evolution approaches
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approaches with automated longitudinal behavioral analysis. Close interactions with electrophysiology and neurophysiology units at the center will enable functional characterization of circuit dysfunction