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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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Max Planck Institute of Molecular Cell Biology and Genetics | Dresden, Sachsen | Germany | about 12 hours ago
: Dresden, Saxony 01307, Germany [map ] Subject Areas: Artificial Intelligence, Machine Learning, Applied Mathematics, Biomathematics, Mathematical Biology, Computational Science, Statistics, Numerical
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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effects on biofilm degradation Genetic engineering of phages and prophages Cloning, expression, and characterization of phage proteins These exciting tasks await you: You will apply microbiological
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natural sciences expertise in materials science and materials engineering, in particular methods of computational material science (e.g. DFT, CALPHAD) programming skills, e.g. Python basic knowledge of data
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scientific journals and conferences Your Profile: A doctoral degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural
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interdisciplinary program, HEIBRiDS trains young scientists in Data Science applications within a broad range of natural science domains, spanning from Earth & Environment, Astronomy, Space & Planetary Research
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Mathematics, Technomathematics, Computer Science, Engineering Informatics, Theoretical Computer Science, Physics Description Description The research group Cyber-Physical Systems of Prof. Matthias Althoff
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14.07.2025, Wissenschaftliches Personal This opportunity is offered by the Chair for Philosophy and History of Science and Technology at the TUM School of Social Sciences and Technology (SOT). ABOUT
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state of Rhineland-Palatinate. Our research focuses on the biology of the cell nucleus and ranges from the molecular level to systems and computational approaches. Researchers at IMB are supported by