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28th October 2026 Languages English English English PhD Research Fellow in Learning-based Control of Autonomous Robot Manipulation Apply for this job See advertisement About the position A fixed
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warmer and wetter world. RAPTURE brings together high resolution physical simulations, machine-learning climate emulators, and new perspectives on the dynamics of weather and climate to understand i) what
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, Julia, C/C++ or similar is required. Experience with machine learning, analysis of climate or high-resolution model output, climate predictions/projections or environmental risk assessment is an advantage
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on addressing this question which lies at the heart of understanding high-impact flooding in an ever warmer and wetter world. RAPTURE brings together high resolution physical simulations, machine-learning climate
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. Qualifications and personal qualities: Applicants must hold a PhD or equivalent doctoral degree within atmospheric physics, meteorology, climate science or machine learning. PhD-students may apply if defence of
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interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning environments, design
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. Project description The PhD fellow will be affiliated with the Scientific Computing and Machine Learning (SCML) research group at the Department of Informatics and supervised by associate professor Anne
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place to study and work. Postdoctoral Research Fellow position within Physics-Informed Machine Learning for Offshore Wind At the Department of Mathematics , there is a vacancy for a postdoctoral research
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environments that aims to address how distributed sensing, fibre-optic monitoring, environmental observations, drone- and satellite-based data, operational infrastructure datasets, and/or machine learning can be
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under