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: Microelectronics; Gordon Roberts ECSE 343: Numerical Methods in Eng; Roni Khazaka ECSE 353: Electromagnetic Fields & Waves; Thomas Szkopek ECSE 354: Electromagnetic Wave Propagation; TBA ECSE 362: Fundamentals
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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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Optimisation Group (PCOG) at SnT, headed by Prof. Grégoire Danoy. PCOG conducts research in parallel computing, search and optimisation techniques, to provide efficient, scalable and robust solutions to state
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, headed by Prof. Grégoire Danoy. PCOG conducts research in parallel computing, search and optimisation techniques, to provide efficient, scalable and robust solutions to state-of-the-art, large-scale
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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partner (HB11 Energy) and existing experimental and theoretical research collaboration. Reporting to Prof. Alexander Fuerbach, you will: Conduct complex numerical simulations and theoretical studies
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) and existing experimental and theoretical research collaboration. Reporting to Prof. Alexander Fuerbach, you will: Conduct complex numerical simulations and theoretical studies of dense laser plasmas