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machine-learning interatomic potentials • Experience with UFPs • Experience with molecular dynamics, ideally with LAMMPS • Contributions to a public code repository Your LIST benefits An organization with a
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
: • Experience developing machine-learning interatomic potentials • Experience with UFPs • Experience with molecular dynamics, ideally with LAMMPS • Contributions to a public code repository Your LIST benefits
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provably powerful learning models for graphs will require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating
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require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating the design and analysis of such models. PhD position
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designs, building effective and conceptual models to inform our theoretical understanding, and developing code and theory frameworks to address new topological phenomena. Depending on the project’s results
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from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position We have a vacancy for a PhD position in the Materials Theory group at the Department
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are at the forefront of knowledge nationally and internationally within their fields and who master the theory and methods of the field. We are announcing three positions within each of the three subject areas of
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Lin and Eirik Rosnes. We invite you to engage with some of the most compelling research challenges at the frontier of quantum information theory and coding. This project aims to elucidate
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 2 months ago
“Gravity at the Extreme: from Theory to Observation”. We are particularly interested in hiring creative, proactive and motivated candidates who flourish at working in a vibrant, interdisciplinary and
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, Electrical Engineering, Computer Science, or a related field. Strong foundation in biomedical signal processing and hands-on experience with clinical data. Knowledge of information theory, sampling theorems