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learning Demonstrated expertise in software and algorithm development, computational methods, data analysis, modeling, machine learning, high-performance and parallel computing, or scientific simulation
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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
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computational methods with a particular focus on deep learning and image analysis. The research is done in close collaboration with the BioImageInformatics Unit of SciLifeLab . SciLifeLab is a national resource
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Martian meteorite falls using advanced correlative microscopy techniques. To determine if they are the same or different Methods We will use a correlative, big data approach that combines X-ray computed
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rates of bioactivity under a range of environmental conditions. Methods A range of techniques will be used to investigate the multidisciplinary project aims. For example: Sample collection and field
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Professorship - Programme Information (PDF, 123 KB) Recently selected Humboldt Professors Our Alexander von Humboldt Professors for AI WANTED: Alexander von Humboldt Professors (female) The Alexander von Humboldt
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theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging, and deployment
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government’s Advanced Modular Reactor (AMR) programme has recently identified HTGRs as the preferred design for future advanced nuclear deployment in the UK, with an aim to deliver a demonstration reactor by the
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capability Fluency in relevant models, techniques or methods and ability to contribute to developing new ones High level of competence in computer programming, with C++ an advantage. Ability to communicate
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science with novel method development, and provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE leadership computing facilities, as well as collaboration