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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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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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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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language models, with hands-on experience in training, fine-tuning, or evaluating state-of-the-art models Solid knowledge in adversarial machine learning or trustworthy AI, including experience with
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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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& 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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need to have a genuine interest in high-quality research and possess strong analytical skills with a scientific approach. As our activities are conducted in an international environment, you must have
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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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analytics Exploring and implementing federated learning and privacy-preserving AI approaches for distributed clinical datasets Collaborating closely with data providers, clinicians, and technical teams
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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