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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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to substantial measurement uncertainty. The group is particularly interested in latent variable modelling approaches to such settings, where the key quantities of interest are not directly observed and
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methods for such searches that account for event selection and systematic uncertainties, and to demonstrate them on realistic searches at scale on national HPC resources. The project will primarily use
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development. The project addresses how advanced digital infrastructure can be used to reduce uncertainty, risk and variation in industrial product realisation. It connects engineering design, AI, digital twins
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and apply probabilistic foresight models for policy-driven technologies and explore the meaning of uncertainty in such projections. Analyse the extent to which policy interventions can accelerate
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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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science. It offers a novel perspective on understanding and managing uncertainty in large-scale infrastructure systems. For more details, see https://research.chalmers.se/en/project/12873 Who we are looking
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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
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than 1,800 employees and nearly 21,600 students. We are looking for a Postdoctoral Researcher in Robotics and Artificial Intelligence (RAI) (https://www.fieldrobotics.eu/ ) based at Department