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learning, and porous materials strong analytical and problem-solving skills ability to work independently and collaboratively in an interdisciplinary team excellent communication skills in English
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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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train deep learning models using agricultural image datasets Collaborate with researchers at the University of São Paulo and agricultural domain experts Publish research findings in peer-reviewed journals
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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student, you will join a highly collaborative, international, and ambitious research community, with access to state-of-the-art computational resources. The positions feature the possibility of going on one
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industrial component given the close collaboration with the quantum computing start-up, Quantum Motion, particularly with Dr. Ciriano-Tejel, machine learning group. Key responsibilities Conduct research
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representation, legislative studies, corruption, political methodology or related topics. You collaborate on research and pedagogical projects and assist in academic administration to some degree. You will teach
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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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combining engineering and life sciences, through challenge-based learning and a multidisciplinary approach in collaboration with hospitals, industry and others, the department addresses the great challenges
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of Safe Learning Based Control for Autonomous Robots in Dynamic