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Field
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). Information Key Responsibilities: Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring. Utilize a mixed approach combining real and synthetic data for algorithm development
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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://www.ukri.org/councils/esrc/career-and-skills-development/funding-for-postgraduate-training/eligibility-for-studentship-funding/ https://www.postgraduate.study.cam.ac.uk/finance/fees https
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learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential inference algorithms as well as proofs of their correctness and efficiency) and systems (e.g
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate