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for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. It comprises Jan-Willem van de Meent, who
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, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. It comprises Jan-Willem van de Meent, who serves as director, Max
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probabilistic frameworks for motion planning in autonomous agents, such as cars or teams of drones. We will work on a fundamental understanding of how autonomous agents can cope with uncertainty and provide means
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the deployment of coordinated flexibility measures. The research topics to be addressed in the project are: How and to which extent can agent-based energy simulations, powered by AI-driven forecasts within a UDT
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: 12 September 2025 Apply now Join the faculty of Geosciences as a postdoctoral researcher! You will work on carbon turnover models and the fusion of data science forecasting methods with process-based
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on carbon turnover models and the fusion of data science forecasting methods with process-based models (hybrid modelling) to map soil and biomass carbon fluxes across Europe at high resolution. Your
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. F., & Pesch, U. (2024). A new carrier for old assumptions? Imagined publics and their justice implications for hydrogen development in the Netherlands. Technological Forecasting and Social Change, 204
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patterns and forecasting future energy scenarios for a specific region for hydrogen. These scenarios feed into the energy optimization models you develop and solve. The goal is to identify factors