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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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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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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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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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the Ice Sheet and Sea Level System Model (ISSM). and/or The development of automated (e.g., machine learning) approaches for mapping glacier-surface morphologies on Mars. You will join a cutting-edge