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Field
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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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both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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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
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strategies (e.g., feature attribution, counterfactual explanations, dialogue-based explanations, hybrid symbolic–ML approaches); develop user-facing explanation interfaces that connect algorithmic reasoning
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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tomography and local adaptive reconstruction to overcome these challenges. Your tasks develop physics-informed, self-supervised learning approaches for phase retrieval implement reconstruction algorithms
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and laboratory free text into structured terminology. The project combines classical text algorithms, medical ontologies, and domain-specific language models, among many other methods and models. You