68 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at Technical University of Denmark in Denmark
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focus on charge injection, ion transfer, and structural dynamics in realistic and model systems for battery materials. The position will span experimental efforts at large scale X-ray facilities, handling
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, structural, and cellular biology; enzymology; immunology; microbial ecology and physiology; natural product chemistry; modelling nanotechnology and bioinformatics. The department mission is, based on a
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including supervision of BSc and MSc students associated with the project As a formal qualification, you must hold a PhD degree (or equivalent). In the assessment of the candidates, consideration will be
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, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex
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research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results
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proteins as food ingredients in food models Conduct project reporting and publishing results in international scientific journals Participating in teaching and supervision of students at all levels As a
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(RAG) models – are shaping professional expertise and practice across diverse Danish public sector domains, especially among frontline workers, including caseworkers, service providers and welfare
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and Mechanical Engineering (DTU Construct). In this role, you are advancing innovation by integrating material science, advanced processing techniques, and modelling to gain a comprehensive
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research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results
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-based simulation model for assessing future mobility technologies in the Greater Copenhagen region. Explore the development of machine-learning based scenario discovery for future mobility policy design