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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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important examples such as Schrödinger Cat states, which are superpositions of coherent states with different phases. This project focuses on the optimal preparation of Schrödinger Cat states in a propagating
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modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux/HPC environments Ability to conduct independent
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, components, overall system performance) Numerical methods and simulation tools (e.g., Python/Matlab, CFD modelling, optimization) Beyond technical skills, we value people who contribute to a healthy and
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, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project The project is in the field of optimization, and in particular the project
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modeling strategies for multiphase flow systems. The PhD topic is on exploring how emerging quantum computing methods can be integrated with classical numerical models to improve the simulation of complex
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; numerical analysis; programming; optimization; stochastic analysis; systems theory. What you will do You are expected to develop your own ideas and communicate scientific results orally as well as in written
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modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux/HPC environments Ability to conduct independent