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. Position You will work actively on the preparation and defence of a PhD thesis focusing on machine learning-based forecasting of renewable energy production, with a particular focus on wind energy. The
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AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials
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to, patient-specific, monthly, daily, and annual QA, machine and equipment acceptance/commissioning, imaging technology, treatment ordiagnostic planning, special procedures, and radiation safety. The focus will
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(target journals: International Journal for Numerical Methods in Engineering – IJNME). Deep learning algorithms for high-temperature multiphase problems (target journals: Computer Methods in Applied
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functional theory. - Effective Hamiltonian methods for quantum phenomena in solids. - Development of machine learning tools for topological materials. - Experimental studies of magnetotransport in quantum
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imaging analysis techniques in biological systems, including cell and tissue samples. • Familiarity with conventional and machine learning based image processing approaches. • In-depth knowledge
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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emphasis on science and engineering research. Learn more and apply Ariel University Department of Physics (https://www.ariel.ac.il/wp/physics/en ) If the link does not work from your country please google
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety