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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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microstructural descriptors that are physically meaningful and predictive. Probabilistic surrogate modelling and digital twin construction. The extracted microstructural descriptors will be used to learn a
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experience or knowledge in one or more of the following areas are also a merit: Space physics Plasma physics Computational physics Applied mathematics and modelling Numerical methods for partial differential
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experience or knowledge in one or more of the following areas are also a merit: Space physics Plasma physics Computational physics Applied mathematics and modelling Numerical methods for partial differential
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carbon cycling, with a focus on boreal forest ecosystems Experience with data processing software for eddy-covariance data (EddyPro, REddyProc) Excellent numeric and analytical problem-solving skills
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probabilistic modelling. The focus will be on quantifying and evaluating uncertainty in both numerical and categorical predictions derived from medical reports, and on integrating these uncertainties
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application of numerical models Assess uncertainties for future sea-level projections Publish results in international peer-reviewed journals Present findings at national and international conferences
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datasets (e.g. ground-based radar measurements and weather station data) Contribute to the development and application of numerical models Assess uncertainties for future sea-level projections Publish
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unique D-MIMO testbed at Lund University, extending existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new
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theoretical understanding of hydrology, boundary layer meteorology, and arctic ecosystem dynamics will be ideal for the planned work. Strong quantitative skills, including programming, numerical modeling and