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disposal strategies and sustainable resource management practices. The project also has synergy the ongoing EURATOM EURAD2 RAMPEC work package, and it is expected that some of the methods developed in HiFi
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live in. Your role Research related to the following areas: Mathematical statistics, Machine Learning, High-dimensional statistics, Robust estimation methods, Probabilistic foundations of mathematical
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Kleitz at the University of Vienna mainly focusses on the development of nanoporous materials, their characterization, and their applications in numerous fields, such as catalysis, adsorption, bio- and
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-method study of human cultural adaptation to environmental challenges with application to invasive plants and tick-borne disease threats among woodland owners with supervision from Dr. Waring and in
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://www.chalmers.se/math/ At the division of Applied Mathematics and Statistics we conduct research within probability theory and its applications, the theory and implementation of finite element methods, inverse wave
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theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging, and deployment
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to reduce the complexity in simulating lake physical dynamics at scale. Borrowing from numerical methods used to design wind turbines and understand turbulent combustion in jet engines, this research aims
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backgrounds and lead annual fieldwork activities for sample collection. Opportunities to travel to conferences and project meetings are also available. This position provides numerous opportunities
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Sciences at Princeton University. The research will be to conduct numerical studies for non-local flow-driven magnetized plasma instabilities and their associated nonlinear transport in astrophysical and
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within