112 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "UNIV" "UNIV" "I.E" Postdoctoral positions in Denmark
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ecological processes, i.e., vertical turbulent diffusion, phytoplankton production and consumption, greenhouse gas emissions, etc., to develop hybrid models. Performance will be compared to several 1D aquatic
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connection of renewable generation, experience with HIL systems and experimental test setups, and strong competences in communication systems as well as coding experience (C++ and Python). As a formal
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required. The position is funded for two years, with the possibility of extension for a third year. Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty
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structure quantification by tomography and imaging Perform testing across different scales, i.e. characterizing the viscoelastic properties of the base material and the nonlinear mechanics of the scaffolds
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. The ideal candidate will have: Experience in developing novel algorithms. Experience in coding in python and preferably C/C++. Experience in frontend engineering, including but not limited
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published papers) with the above topics. A high level of coding competences will be beneficial. If your PhD is not yet completed, please document that your thesis will be submitted before the start date
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. Software or code development, incl. artificial intelligence and machine learning. Automation and robotics, incl. safe human-machine interaction. Serious gaming, incl. AR/VR. Life cycle analysis. You are
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nahuro@sund.ku.dk . Applicants may find the following links useful: Copenhagen Health Complexity Center: https://www.healthcomplex.dk/ University of Copenhagen: http://www.ku.dk/english/ Department
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for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee. You can read about the recruitment process at https://employment.ku.dk/faculty
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and