165 proof-checking-postdoc-computer-science-logic Postdoctoral research jobs in Sweden
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Ref no: ORU 2.1.1-03066/2025 We are looking for up to two postdoctoral researchers in Computer Science, specializing in Cybersecurity and Embodied AI, for a fixed-term position at the School
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develop the techniques and instrumentation, which are used by researchers from magnetism and chemistry to biomedical and environmental science. For information on the beamline, see: SoftiMAX Description
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radioisotopes to track sources and ages of different carbon components. The postdoc will lead the acquisition and analysis of aquatic water chemistry data, collected in different Arctic settings such as northern
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of Computer Science, Electrical and Space Engineering as part of the national research program WASP. Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a
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at the MAX IV Laboratory in Lund, Sweden. MAX IV is a world-leading synchrotron facility, providing researchers with cutting-edge tools to advance knowledge across materials science, life science, chemistry
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build the sustainable companies and societies of the future. The Robotics and Artificial Intelligence subject (RAI) (www.ltu.se/robotics) at the Department of Computer Science, Electrical and Space
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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Exciting postdoc opportunity in integrating bioelectronic technologies in plant science. Work assignments Are you passionate about addressing climate change and enhancing agricultural productivity
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the following fields: mathematics, physics, computational science, or engineering, and have a strong background in numerical analysis and scientific computing. Required qualifications include basic knowledge
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description