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Description Are you curious how Deep Learning and Online Learning can be effectively combined to create new learning paradigms? Job description Online learning algorithms achieve robustness often at the expense
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for both youth and police. You will collaborate with stakeholders, end-users, game designers and our CONTEXT science team to improve our virtual reality game and its biofeedback algorithms. You will perform
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, numerical analysis, and implementation of novel numerical algorithms to efficiently solve parabolic PDEs such as the heat equation. More precisely, you shall investigate space-time finite element methods (FEM
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virtual reality game and its biofeedback algorithms. You will perform user-interaction tests in target groups and record and analyse psychophysiological measures of autonomic nervous system balance during
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation
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PROTEUS. Ultimately, the causal reasoning and uncertainty algorithms you build will serve as the quantitative engine for the "Copernicus Agent," an AI assistant designed to give European policymakers
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, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good programming skills in Python/C/C++ Good oral and written skills in English
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on practical feedback linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and
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-of-information metrics. Propose computational algorithms to estimate these metrics. Design and execute simulation studies to evaluate the above. Develop and test statistical software. Write user-friendly guidance