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of the AI algorithms. Key duties Develop a robust framework to simulate streamflow decomposed into fast-flow and baseflow at multiple Moroccan watersheds. The candidate would have to test various fast-flow
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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concerned with optimal transport for inverse problems. Optimal transport for inverse problems One of the central topics of the research projects is the further development of theory and methods
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Responsibilities The appointee will join a multidisciplinary team led by the Principal Investigator to develop AI‐driven navigation systems in robotic implant surgery. The duties include but not limited
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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a
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the acceleration of relativistic plasma in jets. Developments of new automated algorithms for VLBI model-fitting, kinematics measurements and robustness assessment. 2. Probing the physical mechanism of neutrino
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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, physics, or a medical imaging related field. Experience with developing advanced pulse sequences or accelerated acquisition and reconstruction algorithms will be highly valued. Interested candidates should
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address complex environmental challenges. About the Project The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies