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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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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD
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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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quality, avoiding the paralysis that troubles artificial algorithms when options seem equally good. This project asks: what objective functions do such biological systems optimise, and how can we use
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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
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are inherently highly complex. In this research project you will use state of art AI-based optimization algorithms to develop new functionality into industry-relevant digital design tools (CAD) to support
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Model Based Design and Flight Testing of a Vertical Take-Off Vertical Landing Rocket (C3.5-MAC-John)
tested will have applications for landing on other planets or moons, or even propulsive landing of rocket stages on Earth. These missions require the use of novel guidance algorithms, sensors, and control
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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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. Without these guarantees, the algorithms will remain limited to experimental testbeds. The aim of this project is to address this limitation by combining the complexity of deep-learning control policies