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techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
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of singular foliations, working with advanced tools like groupoids, cyclic cohomology, C*-algebras and K‑theory. The Department of Mathematics is seeking a highly motivated PhD candidate for a research position
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for load forecasting in scenarios where current models fall short, such as extreme weather events, grid incidents and high variability in renewable energy. You will explore techniques including graph neural
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Mathematics Department as a PhD candidate and explore cutting‑edge invariants of singular foliations, working with advanced tools like groupoids, cyclic cohomology, C*-algebras and K‑theory. The Department
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postdoctoral researchers of WP1 and WP2. Your work will be theory- and data-driven, contributing to both fundamental research and regulatory applications. Given the empirical background of our research group
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, Dr Aafke Schipper, and one PhD candidate, and also collaborate with the PhD candidates and postdoctoral researchers of WP1 and WP2. Your work will be theory- and data-driven, contributing to both
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of the newly developed methods. For this you will interact extensively with other work packages in the project that focus on neuroimaging, ecological momentary and physiological assessments, theory and ethics
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with other work packages in the project that focus on neuroimaging, ecological momentary and physiological assessments, theory and ethics, as well as societal impact. You will have the opportunity
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, such as heat stress in reproduction, mathematical modelling of plant development and regulatory mechanisms, and single-cell and biomechanical studies. In addition, it is expanding its research and education