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to contribute to a better understanding of the mechanisms underlying intratumor heterogeneity, its dynamics during progression & therapy response and its complex interaction with the tumor microenvironment
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better understanding of the mechanisms underlying intratumor heterogeneity, its dynamics during progression & therapy response and its complex interaction with the tumor microenvironment. The Department
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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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from biological questions and challenges that are addressed using state-of-the-art and novel computational and AI strategies, through close interactions and iterations with biological experiments and
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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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engineering, and computational biology, the bioinformatician will contribute to the implementation, automation, and scaling of NGS‑enabled analytics and AI‑driven nanobody–antigen structure prediction and
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engineering, and computational biology, the bioinformatician will contribute to the implementation, automation, and scaling of NGS‑enabled analytics and AI‑driven nanobody–antigen structure prediction 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