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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | about 6 hours ago
Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about Artificial Intelligence, Machine Learning
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https://www.academictransfer.com/en/jobs/357205/2x-phd-positions-in-the-mathema… Requirements Specific Requirements You have, or will shortly, acquire a Master's degree in either Mathematics
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Biophysics: Learning in living adaptive networks Biophysics: High-resolution structural
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Description Start date/duration 15.02.2026 for 4 years Job profile: Your tasks: Motor control and/or motor learning with regard to postural control or stability during the execution of dynamic movements, which
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Biophysics: Learning in living adaptive networks Biophysics: High-resolution structural
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at the University of Twente is looking for two PhD candidates to join the research team of Dr. Gaurav Rattan. The positions are funded by the NWO VIDI project Learning on Graphs: Symmetry Meets Structure (LOGSMS
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Additional Information Work Location(s) Number of offers available1Company/InstituteAI LEARN centreCountryNorwayCityBergenGeofield Contact City Bergen Website https://www.uib.no/en/charterandcode Street
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a