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Field
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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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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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to learn are essential. Solid programming skills in Python and/or R; experience with reproducible workflows (Git, Snakemake/Nextflow, containers) is a plus. Interest in cancer biology, tumor microenvironment
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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
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essential, while experience with machine learning is advantageous but not strictly required. Excellent English skills, both in verbal and written communication, are required for the project. We are looking
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of separating fire-induced signatures from natural environmental variability (weather, canopy changes, tree motion) and fluctuations in the SoO sources themselves. Machine-learning methods will help improve long
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properties through tuning of concentrations and types of viscosity modifiers and superplasticizers, deep learning modeling of parameters of cement composites. The project is realized at the Bydgoszcz
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specific research Centre, of which there are twelve totals. Information regarding the research Centres, their activities and production are available at http://www.fbk.eu/research-centers . In particular
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22.12.2025, Wissenschaftliches Personal We are an interdisciplinary team at the Chair of Safety, Performance and Reliability for Learning Systems, and we are looking for exceptional PhD candidates