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to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models (possibly based on machine learning) can be
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-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models
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to machine learning and artificial intelligence is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills. Applicants must be
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. Familiarity with research relating to machine learning and artificial intelligence is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative
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(for instance mathematics, statistics, medicine with a strong emphasis on computational and programming aspects). Experience in machine learning is a requirement. Experience with programming and app development
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computational and programming aspects). Experience in machine learning is a requirement. Experience with programming and app development will be emphasized. Personal skills, including independence, abilities
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(BEM) is an advantage. Experience with either scientific computing or numerical optimization is an advantage. Experience with unsupervised machine learning is also an advantage. Applicants must be able
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engineering, cybernetics, applied mathematics or another relevant degree at the same level, and applicants must also have a specialization in bioinformatics, machine learning/artificial intelligence, simulation
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. In particular, we are interested in the joint applicability of such models and to what extent simpler models (possibly based on machine learning) can be integrated into full-physics simulation
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on machine learning) can be integrated into full-physics simulation. The successful candidate will contribute to research activities in the CSSR center, and in particular its work package 5. Tasks