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Field
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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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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analytical and problem-solving skills, as well as a commitment to advancing research, are essential You recognize yourself in the following profile: You hold an MSc degree (or equivalent) in molecular life
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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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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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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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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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an analytical-computational framework for a “living” 4D Digital Twin (3D + time) to enable automated progress monitoring and multi-domain safety assessment in civil engineering applications. The project focuses