42 parallel-and-distributed-computing Postdoctoral positions at Aarhus University in Denmark
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? Then the Department of Electrical and Computer Engineering invites you to apply for a 2 year postdoc position bridging research with industrial implementation and innovation. Expected start date and
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advice, and education. We offer professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international
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laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine
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for environmental challenges, are also encouraged to apply. The postdoctoral researcher will work within an interdisciplinary team of computational and applied scientists. The work will be done in close collaboration
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The project will involve both experimental and computational work and the candidate is expected to be comfortable with both. The candidate is expected to have (or be close to finishing) a Ph.D. in molecular
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-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine research sections with
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professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department
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counselling to expat partners. Read more here . Please find more information about entering and working in Denmark here . Aarhus University also offers a Junior Researcher Development Programme targeted
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(including gastruloids) to study epigenetic regulation in development. An interdisciplinary environment with opportunities for collaboration across experimental and computational life sciences within MBG and
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will