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design scalable numerical methods for quantum master equations, implement high-performance simulations, and help build open-source tools for large-scale spin-system modeling. By improving our ability
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English. The PhD must have involved numerical modelling. It is desirable to have documented knowledge from their university education in: Mathematics, especially differential equations. Numerical methods
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their university education in: Mathematics, especially differential equations. Numerical methods and/or computer programming. Cloud physicsIt will be advantageous if the applicant is experienced with modeling
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proficient in the project related areas, and in particular regarding: Machine Learning, Numerical Methods, Probability Theory, and Bayesian Methods and MCMC Algorithms. Proficient programming skills is a
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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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focuses 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
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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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. The DC will use pore-scale direct numerical simulations (based on the lattice-Boltzmann method) to enable the precise quantification of heat transport within noobed electrode microstructures, to then