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using existing NPL datasets. The work will integrate suitable physics-based models (for example PV performance modelling, electro-thermal and thermofluid dynamics) with deep learning and multi-fidelity
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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. Melodia, SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control, IEEE INFOCOM 2026 [3] A. Duttagupta, M. Jabbari, C. Fiandrino, M. Fiore, J. Widmer, SymbXRL: Symbolic
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, machine learning, deep learning, high powered computing (requiring Python etc) or a combination of data science and qualitative methods (e.g., interviews and focus groups). Project themes include (but
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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expected to learn basic R or python to generate plots and analyze data. Qualifications Requirements for employment are: You have deep knowledge of immunology including autoimmunity You have experience
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through