143 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" 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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years: https://www.daad.de/en/information-services-for-higher-education-institutions/further-information-on-daad-programmes/gssp/ Doctoral candidates who are admitted but not awarded a scholarship will
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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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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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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to mlcv at tu-dresden.de or to: TU Dresden, Chair of Machine Learning
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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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and training provision within CAFE-BIO are available from the network website ( https://cafe-bio.org ) and the official EU page for the network ( https://cordis.europa.eu/project/id/101226762