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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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learning; convex analysis; functional analysis; mathematical statistics; numerical analysis; programming; optimization; stochastic analysis; systems theory. What you will do You are expected to develop your
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decision is made Strong background in computational modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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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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results demonstrate that compact heat-exchanger solutions—supported by conceptual design and aerodynamic optimization of integrated ducts—can deliver substantial reductions in specific fuel consumption and
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), numerical optimization, and inverse problems. Strong emphasis will be placed on personal qualities such as the ability to work in a team and to communicate effectively, as well as a strong analytical ability
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well as dissimilar combinations has taken shape with the aid of numerous funded projects implemented in close co-operation with industries. However, there has been very limited previous work globally addressing
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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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experimental, pilot-scale, or high-fidelity simulation data into model calibration and validation workflows Design and run numerical simulations of multiphase flow systems and reactors Quantify model uncertainty