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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
will be linked with STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics. STADIUS excels in combining fundamental and applied research. It develops mathematical engineering tools
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conformational landscapes and regulatory mechanisms Integration of structural and functional data to uncover novel regulatory interfaces Collaboration with leading academic and industrial partners across Europe
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Computational Biology, Bioinformatics, Computer Science, AI/Machine Learning, Physics, Mathematics, Bioengineering, Structural Biology, Biophysics, or a related field Strong quantitative, analytical
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Computational Biology, Bioinformatics, Computer Science, AI/Machine Learning, Physics, Mathematics, Bioengineering, Structural Biology, Biophysics, or a related field Strong quantitative, analytical
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analytical and problem-solving skills Motivation to learn computational protein design methods Excellent written and spoken communication skills in English Ability to work both independently and
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analytical and problem-solving skills Motivation to learn computational protein design methods Excellent written and spoken communication skills in English Ability to work both independently and
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from large‑scale immune repertoire data and in translating these insights into rational, model‑driven prioritization of high‑quality nanobody candidates. Working at the interface of immunology, protein
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from large‑scale immune repertoire data and in translating these insights into rational, model‑driven prioritization of high‑quality nanobody candidates. Working at the interface of immunology, protein
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sequencing data generation and interpretation Bioinformatics and statistical analysis, to characterize genome evolution, mutation processes, and adaptation patterns The PhD candidate will progressively
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sequencing data generation and interpretation Bioinformatics and statistical analysis, to characterize genome evolution, mutation processes, and adaptation patterns The PhD candidate will progressively