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provides professional education nationally and internationally, supporting lifelong learning. M2 strives for close collaboration between academia, industry, and society, focusing strongly on practical
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in artificial intelligence (AI) to join our growing biomedical innovation team. In this pivotal role, you will lead and contribute to the design, development, and deployment of machine learning
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++, Fortran) Background in mechanics of materials or computational modelling Experience with machine learning for physical fields/PDEs/GNNs and HPC workflows Interest in interdisciplinary collaboration and
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experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
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automata, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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data types (transcriptomics, proteomics, imaging). AI/ML Applications: Applying machine learning or AI to predict gene function or discover functional relationships from perturbation data. FAIR
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, which provides a fantastic opportunity to learn how to run challenging development projects. Qualifications We are looking for analytic and motivated colleagues that can perform advanced research, both
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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opportunity to learn how to run challenging development projects. Qualifications We are looking for analytic and motivated colleagues that can perform advanced research, both independently and in cooperation
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communication theory, machine learning, complex networks, and optimization. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three