27 machine-learning "https:" "https:" "https:" "https:" uni jobs at Chalmers University of Technology in Sweden
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-energy devices. Using state-of-the-art electronic-structure calculations and machine learning methods, you will model these effects and contribute to the design of improved semiconductors for solar cells
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We invite applications for a Doctoral student position in applied mathematics and machine learning for urban 3D reconstruction, within the Digital Twin Cities Centre (DTCC). The project aims
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. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application to medical problems. About the
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to the forefront of quantum technology, and to build a Swedish quantum computer. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and
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learn to combine modern analysis techniques like Morawetz estimates with Penrose's Nobel prize winning geometrical insights and formalisms, intricate symmetry operators, spinor techniques and powerful
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computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project
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computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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and mixed-signal design, and thus broaden and strengthen our expertise in the design of electronic systems. You will also develop and teach courses in electronics design at the bachelor and master
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researchers develop new machine learning (ML) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division , our group