112 density-functional-theory-molecular-dynamics Postdoctoral research jobs in Sweden
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- University of Lund
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researchers, are top-ranked internationally. Work description The employment mainly comprises research in the intersection of Generative AI and software engineering, within the scope of the research project
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with different research profiles and research schools. Our work takes place in cooperation with and in strategic agreements with companies, organisations and public authorities in the region. Employment
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methods, technologies, and materials to reduce reliance on unsustainable practices and fossil fuel-based compounds as a response to societal requests for green alternatives that maintain high performance
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communication by participating in national and international meetings, and support service for analytical equipment. Publishing popular science and scientific articles is a central part of the work
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. MC2 houses a cleanroom for micro- and nanofabrication with the latest equipments. Our work is often done in close collaboration with Swedish and international partners within academia, industry and
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products. The project will be carried out as a part of the newly established BioGlue-Centre “Competence Centre for Bio-based Adhesives”, which is a Vinnova Centre of Excellence in Sustainable Industry and
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and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
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universities in Sweden. The vision is a sustainable future through materials science. Read more at WISE Duties As a post-doctoral researcher, you are expected to perform both experimental and theoretical work
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We invite applications for several postdoctoral research positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment
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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