136 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" positions at DAAD in Germany
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programming task within the thematic context of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bi-molecular-machine-learning/ Employment conditions This is a
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Newton`s method. In
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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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data analysis, network analysis, or machine learning is a plus Good organisational skills and ability to work both independently and collaboratively Effective communication skills and an interest in
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group has used their expertise in computer simulations on small model chromosomes to demonstrate that polymer-assisted condensates are capable of maintaining the epigenetic state through 40 generations
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programming task within the thematic context of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bi-molecular-machine-learning/ Employment conditions This is a
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Back to Overview Research Assistant / PhD Student (m/f/d), Machine learning chiral molecules, 75%Full PhD Working LanguageGerman, English LocationKassel Application Deadline20 Feb 2026 Starting
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numerical groups More information on the Junior Research Group can be found under https://tu-dresden.de/mn/physik/itp/das-institut/ag/arbeitsgruppe-quantum-critical-matter?set_language=en . If you have
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data