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Computational Astrochemistry/Algorithm development for Quantum Dynamics Calculations School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof AJHM Meijer Application
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Computational Circular Design: Development, scalability and computational efficiency of surrogate-assisted many-objective optimisation algorithms for circular design for disassembly (C3.5-AMR
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Computational Circular Design: Development, scalability and computational efficiency of surrogate-assisted many-objective optimisation algorithms for circular design for disassembly (C3.5-AMR
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into their training process. By fusing the theoretical rigour of control theory with the flexibility of deep learning, the goal is to develop a new framework to train magnetic control algorithms for tokamak reactors
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, autonomous learning agents are likely to take an active role in human society, engaging in daily interaction and collaboration with humans. Developing learning algorithms that enable these agents to produce
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studies, and international governance. Essential skills include solid computational abilities in Python or MATLAB for developing material accountancy algorithms and running Monte Carlo simulations, along
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problems. A key aim of this PhD is therefore to develop novel IRL algorithms tailored to this setting, capable of extracting hidden reward functions from real-world, imperfect data. This will allow us to ask
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is involved in the DUNE project with a broad range of responsibilities in detector construction, modelling and software development. This PhD project will focus on the development of a methodology for
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also be involved in improving object reconstruction, such as developing advanced algorithms for tau lepton identification or jet substructure / particle-flow techniques to identify Higgs bosons decaying
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extraction? What would be the best choice of solvent? What is the optimal route to recycle the water in the fermentation broth? Answering these questions requires us to develop new design algorithms. It is