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river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological
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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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of this project for a one-year period (100% full-time commitment) to make a significant contribution to the implementation of machine learning (ML) algorithms. The postdoc is expected to have proven experience in
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work. Availability for a ≥4 month stay abroad. Background in at least one of: AI/machine learning, computational modelling, microscopy, or cell/molecular biology. LanguagesENGLISHLevelGood Additional
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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structures, access to space, multidisciplinary design and concurrent engineering, uncertainty treatment and optimisation, machine learning. (https://www.strath.ac.uk/ ) Task description for your Individual
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for mitigation of marine-induced hazards, application of machine learning techniques and opportunities provided by AI. Even though the listed advanced topics are mostly at the cutting edge of fundamental research
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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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the European Research Council - ERC COG 101088763. The work for this position is in the area of Machine Learning and Natural Language Processing. We are offering We offer a challenging position with the
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing