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Efficient and Reliable Numerical Solution of Dynamic Optimization School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Yuanbo Nie Application Deadline: Applications
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/EXPERIENCE: Numerical machine leaning/Scientific Machine Learning are highly expected Preferred research directions: Numerical Optimization and Scientific Machine Learning. A PhD in Mathematics or Applied
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Description Mathematical modeling forms the basis for understanding, simulating, optimizing and controlling numerous scientific phenomenon and associated measurements. Mathematical models frequently take the
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for motivated students in the field of numerical fluid mechanics and multi-physics simulation methods. Be part of change Analyze and optimize existing coupling strategies to improve the efficiency and scalability
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RAP opportunity at National Institute of Standards and Technology NIST Applied Optimization and Simulation Location Information Technology Laboratory, Applied and Computational Mathematics
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simulation and optimization of PDE-based problems involving uncertainties or stochastic components. We are looking for an internationally recognized individual who is a leader in the field of Numerical
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called Tiramisu [1]. Unlike existing frameworks, Tiramisu can perform advanced code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly
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support research activities in power system resilience optimization and asset health modelling. The role focuses on developing data-driven and physics-informed analytical models to enhance reliability, risk
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optimization of technical processes and products. The efficient solution of large linear systems of equations is a key challenge in numerous scientific and industrial applications – from numerical simulations
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will be extended to three-dimensional roughness configurations, requiring the development of dedicated numerical tools optimized for high-performance computing environments. Results will be