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involve directed evolution and protein optimisation, applying molecular biology and biophysics. Researchers will be supported to develop skills in the latest AI or machine learning tools for protein design
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, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
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, modelling and machine learning to improve defect detection, classification and power loss simulations. Benchmarking field-acquired images with laboratory measurements. Publishing results in leading journals
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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28.10.2025, Wissenschaftliches Personal Open PhD/PostDoc position (TV-L E13) in Cyber-Physical Systems at the Technical University of Munich – Heilbronn Campus to start as soon as possible. Focus
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD degree (or foreign equivalent) in Electrical Engineering or a closely related field in hand by
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pathways. Applicants must hold a PhD in engineering, climate science, energy systems, or a related field. Demonstrated expertise in whole energy system modelling or power systems analysis is a strong asset
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PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitative field * Demonstrated expertise in programming and scripting (e.g. R, Python, Bash) for data
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, and research interns, and will expand with additional postdocs and PhD students early 2026. The laboratory is physically located at Kemicentrum in Lund and is also part of the vibrant NanoLund