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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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, 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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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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ensures safe operations. Our activities are related primarily to development and application of numerical modelling, e.g. CFD, FEA, FSI, optimization, ML, but we are also involved in both experiments and
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We are seeking a highly motivated doctoral student to develop ship physics-integrated machine learning models for real-time prediction and optimization of wind-assisted ship propulsion systems
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. Background in numerical analysis and/or optimization, with interest in error estimation, convergence, and robustness to noise. Experience with geospatial data (LiDAR/photogrammetry/GIS) and handling large