102 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at Chalmers University of Technology
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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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information about the Akelius Math Learning Lab, see: https://www.chalmers.se/institutioner/mv/akelius-math-learning-lab/ Who we are looking for The following requirements are mandatory: Doctoral degree in
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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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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interest in research team-work Industrial Experience from relevant industries Experience in computer based engineering and/or model based systems engineering tools What you will do Participate as researcher
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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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of Biosciences Contract terms The Doctoral student positions are fully funded from start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position