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Field
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and its supporting infrastructure • realize on-chip pulsed DNP • design microwave pulse sequences for on-chip DNP using spin physics, numerical simulation tools, and quantum optimal control
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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About the project: Multiscale Molecular Dynamics: a mathematical exploration of coarse-graining Supervisor: Dr Thomas Hudson, University of Warwick When applied to molecular dynamics simulations
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed
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limited, noisy, and low-resolution measurement data? In this project, you will develop a novel physics-informed machine learning approach that integrates physical simulations of the measurement process with
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spin-lattice dynamics simulations, a framework that combines atomistic spin dynamics (magnons) with molecular dynamics (phonons), to investigate ways of manipulating spins in various magnetic systems
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-based metrics. Your work will be validated through simulations, phantom experiments, and first-in-human volunteer studies in close collaboration with clinical partners. You will work in a highly
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numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific computing environment. A fundamental
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Position – Swimming in Complex Environments The Institute for Advanced Simulation - Theoretical Physics of Living Matter (IAS-2
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, sustainability objectives and future uncertainties into a unified decision-making framework. The candidate will develop probabilistic modelling and stress-testing approaches, including Monte Carlo simulations and