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@au.dk) Applicants must have a relevant PhD degree in biology, biogeochemistry, hydrology, glaciology, oceanography, geoscience or physics. Field experience, data analysis and programming (e.g., python
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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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. The candidates must hold a PhD in Chemistry/Physics. Experience in data framework development, kinetic/thermodynamic modeling, and collaborative interdisciplinary research. An education history in chemical
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-year extension. The project is fully funded by the Independent Research Fund Denmark (DFF). The main objective of this project is to develop physics-constrained, data-driven turbulence models
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and around 825 students are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD students have high academic ambitions and deliver high-quality results for both
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will report to Professor of Medical Physics Stine Korreman. Your competences You have academic qualifications at PhD level, for example within the following areas; computer science, biomedical
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. Qualifications We are looking for a highly motivated researcher with a background in atmospheric chemistry, physics, mathematical modelling, climate science, or related fields. The ideal candidate should have
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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals
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spatially explicit process-based ecological model (DEB-IBM) for muskoxen in the high-Arctic through the analyses of long-term GPS and acceleration data. using the model to estimate the (cumulative) impacts
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biodiversity assessments and methodological development at Aarhus University. Your profile Applicants should hold a PhD in ecology, population or conservation genetics, evolutionary biology, bioinformatics