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-compliant data infrastructures that enable secure data sharing, reuse, and AI-driven analytics Exploring and implementing federated learning and privacy-preserving AI approaches for distributed clinical
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for operational decision‑making, interactive design, or control‑in‑the‑loop visualisation. Machine‑learning surrogates offer speed, yet purely data‑driven models often extrapolate poorly and may violate physical
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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& Solar Power Plants Battery Energy Storage Systems (BESS) Smart Grids & Power Systems Sustainable buildings, communities and cities[EC1.1] Artificial Intelligence & Machine Learning for Energy Digital
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analytics, geographical information science, satellite positioning, ubiquitous positioning, photogrammetry and remote sensing, geodesy and geodynamics, environmental monitoring, geo-hazards studies
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condensed matter physics • Ability to learn and develop skills in analytical computation, theoretical modelling and numerical simulations, in particular the numerical solution of partial differential
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conferences Your profile - Master?s degree in physics, mathematics, computer science, or a related field - strong analytical skills and solid theoretical background - experience in machine/?deep learning and
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outcomes research and real-world data analytics, with a strong publication record. Proficiency in advanced data analytics, machine learning, and statistical modeling. Job Description: The Department
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026
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