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collaborate with members of different research groups within DTU BRIGHT or beyond and teach and supervise BSc and MSc students. Key selection criteria: Experience in microbiology, metabolomics, systems biology
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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, academic, and industrial relevance Contribute to and take leadership at different stages of project development Conduct and in time lead research projects with high visibility in the international networks
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: Conceptualisation and theoretical development of spatiotemporal optimisation of biodiversity dynamics during recovery. Synthesising and modelling and species pools and biodiversity patterns given different
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to quantify the uncertainty in model outputs using different methods. Run scenario analysis to identify management practices with the largest mitigation potential, both spatially and temporally Support training
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on retrospective Danish data. The research will include testing different levels of model scaling in terms of data amount and diversity, and training will take place both on a local GPU cluster and on the Gefion
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and conducting laboratory work. Insight into applied mathematics, linear algebra, process-based modeling, and soil health indicators. Experience with Python, applied statistics, and gradient-based
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analysed. Analytical Modelling and Simulation: Development of constellations in different orbits and under multiple geometries for the evaluation of Key Performance Indicators (KPIs) related to visibility
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) multiscale fluid simulation methods (CFD, LBM, PNM, MD) scientific programming (Python, C++, git) microfluidic experimentation Why Join SDU Sønderborg? The SDU Energy Systems Group at CIM is a dynamic