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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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, synthetic data or data-driven decision support uncertainty quantification, robustness, variation simulation or tolerancing CAD/CAE integration, geometry assurance or quality data automation, control, 5G/6G
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking