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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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available1Company/InstituteDepartment of Computer ScienceCountryNorwayCityTrondheimPostal Code7491StreetHøgskoleringen 1Geofield Contact City Trondheim Website http://www.ntnu.no Street Høgskoleringen 1 Postal Code
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as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the project Can AI interpret graphs like a human materials scientist
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians Aalto University is where science and art meet technology and business. We
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, statistical methods, or machine learning is considered a merit. Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University´s rules and guidelines
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
to accelerate technological maturity (TRL), validation, and the transfer of scientific results. Main Responsibilities: Application of advanced Machine Learning, Deep Learning, reduced-order models, and physics
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SINTEF, Equinor, and Total. The main objectives of the project include the development and the integration of signal processing and machine learning methodologies aiming to improve flow assurance via field