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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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modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications. Biological applications and disease areas ideally focus on genetics and
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must be ranked favorably by both CCB and Oncology. Research You will lead a ground-breaking scientific program addressing basic research questions with a pronounced translational potential. You have a
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by both CCB and Oncology. Research You will lead a ground-breaking scientific program addressing basic research questions with a pronounced translational potential. You have a collaborative mindset
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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next‑generation sequencing (NGS), quantitative repertoire analysis, and AI‑based structural modeling. The successful candidate will play a central role in extracting biological and structural insight
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next‑generation sequencing (NGS), quantitative repertoire analysis, and AI‑based structural modeling. The successful candidate will play a central role in extracting biological and structural insight
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computational design tools to engineer novel membrane proteins, then validate these designs experimentally, with the goal of integrating them into functional devices at the biology–electronics interface. Research
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computational design tools to engineer novel membrane proteins, then validate these designs experimentally, with the goal of integrating them into functional devices at the biology–electronics interface. Research