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Mathematics (analysis, numerics, modeling) or in a comparable program with a strong mathematical focus and knowledge in, for example, functional analysis as well as the theory and numerics of PDEs. Strong
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stratification, biomarker identification, and translational tools for precision oncology. Job-ID: V000014820 Field of application: Institute for Computational Biomedicine Location: Heidelberg Job Category: Science
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Field of application: Molekulare Virologie Location: Heidelberg Job Category: Science and teaching Working hours: full-time (100%) Published: 21.10.2025 Limitation:Temporary (36 Monate/ extension wanted
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: V000014739 Field of application: Klinik für Kardiologie, Angiologie und Pneumologie Location: Heidelberg Job Category: Science and teaching Working hours: full-time (100%) Published: 10.10.2025
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: Heidelberg Job Category: Science and teaching Working hours: full-time (100 %) Published: 28.10.2025 Limitation:Temporary (24 months, extension is sought) Contract:TV-L Your Profile PhD in a relevant field (or
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: Dynamical Systems Control Theory Formal Methods Reachability Analysis Computational Geometry Context The applicant will be directly advised by Prof. Matthias Althoff (https://www.ce.cit.tum.de/cps/members
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the Gilsbach lab . Job-ID: V000014326 Field of application: Institut für Experimentelle Kardiologie Location: Heidelberg Job Category: Science and teaching Working hours: full-time Published: 29.10.2025
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random
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: Heidelberg Institute of Global Health Location: Heidelberg Start date: at the earliest possible date Job Category: Science and teaching Working hours: full-time Published: 05.12.2025 Limitation:Temporary
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. Your qualifications An excellent PhD degree either in Computer Science, Physics, Mathematics or related fields, ideally with a background in quantum theory, quantum computing or quantum machine learning