404 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" uni jobs in Sweden
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- Karolinska Institutet, doctoral positions
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- University of Gothenburg, Department of Education, Communication and Learning
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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 up to five
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience
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, workplace learning, educational leadership, communication, school development, didactics and teaching. For more information about doctoral education in education at the University of Gothenburg: https
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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lecturer who can complement our team with expertise in gender studies that is clearly oriented towards global studies and/or human rights, and who can teach, supervise and communicate with students in
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experience in manufacturing systems modeling, simulation (i.e., DES), and digital twins. • Good knowledge and experience in machine learning, reinforcement learning, and AI-based optimization for production
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computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
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(e.g., from the viewpoint of physics, chemistry, or mechanical engineering), programming, machine learning, or equipment automation (including microfluidic systems, robotics and remote sensing
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optical microcavities, and similar lines, summarized in these publications: https://www.pnas.org/doi/abs/10.1073/pnas.2505144122 https://www.science.org/doi/full/10.1126/sciadv.adn1825 https
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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