168 computational-physics-"https:"-"https:"-"https:" Postdoctoral positions in Denmark
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Environment. CEBE is a strategic initiative funded by the Villum Foundation that seeks to strengthen research and development of sustainable solutions for the construction sector. The programme will run for 10
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, proteins and DNA origami constructs, and computational procedures for data analysis. The project is a collaboration between the single molecule biophysics and chemistry group at iNANO/Department
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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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friendly atmosphere of large and interacting communities for e.g. structural biology, molecular cell biology, computational biology, neurobiology, and molecular medicine that encourages lively, open, and
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computational scientists. The position offers a unique opportunity to work at the interface of landscape ecology, biodiversity science, climate mitigation, and sustainable agriculture, contributing directly to
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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
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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
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that addresses these issues. The center brings together experts on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University
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of computer-aided tools for chemical and biochemical product and process modeling, process synthesis, design, analysis and operation. The tools are applied in the chemical, petrochemical, pharmaceutical
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model