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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning methods with the support of
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theoretical analysis, implementation of methods in computer codes, use of state-of-the-art high-performance computers in Sweden and in Europe, application of machine-learning and AI techniques, and
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on developing a fundamental understanding and numerical models for multiphase flows, which are crucial for various industrial processes. The successful candidate will develop advanced physics-based methods in
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flow, fluid dynamics, and sustainable energy systems. The research focuses on developing new methods to study and model multiphase flows as key phenomena in energy and industrial processes. The work
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networks (CNNs), which identify local correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit
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the DC will use pore-scale direct numerical simulations (based on the lattice-Boltzmann method) to enable the precise quantification of mass transport within electrode microstructures, reconstructed via X
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methods in fluid dynamics and heat transfer to study multiphase flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict
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handful of them have excited states with the opposite parity as the ground state. Hence, in most cases, traditional spectroscopic methods cannot be applied to negative ions, leaving many of them poorly
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that lymphopenia is associated with decreased overall survival in cancer patients. Currently, there are no established methods that enable the modification of radiotherapy protocols to prevent lymphopenia without
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capabilities of nonlinear quantum systems, employing tools from quantum information theory and quantum metrology. The work will involve learning and applying mathematical methods to solve open quantum dynamics