70 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at Technical University of Denmark
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/sintering of functional ceramics Have knowledge of SOEC materials and technology As a formal qualification, you must hold a PhD degree (or equivalent) in mechanical engineering, materials science, or a
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PhD students working collaboratively at the interface of physics and Earth science. This project offers a rare opportunity to engage in groundbreaking research with high impact across both academic and
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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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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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The project’s focus will be on modeling novel silicone polymers and elastomers and developing mechanical design for storage of energy in the form of large elastic deformations. A close collaboration with chemists
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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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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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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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on regulatory mechanisms of cell signalling in several cellular models. The team combines omics technologies with bioinformatics, and functional validation of candidates by biochemical, cell biology, and imaging
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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