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”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward
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., manuscripts for publication and grant applications. Plan for specific aspects of the research programme. If given a particular hypothesis to examine, plan for own contribution up to 3 months ahead
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Improving Deep Reinforcement Learning through Interactive Human Feedback School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Bei Peng, Dr Robert Loftin Application
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fixed-term 301 Tutors Ensure the core Academic Skills programme is cognisant of and responsive to the needs of international students Provide guidance to staff as part of the Elevate staff development
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/interview) A degree (Bachelor’s, Master’s, or PhD or equivalent experience) in Software Engineering, Computer Networks, Embedded Systems, AI, Data Engineering, or a related subject (assessed at: application
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. (assessed at: application & interview) Desirable criteria Knowledge of computer programming with experience in a scientific program language e.g., Python, Java, C++, C, C#, LabVIEW, MATLAB, Halcon, R, Maple
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be used to demonstrate the ability to detect both gradual degradation and faults in the machine. Training & Skills You will benefit from a taught programme, giving you a broad understanding of the
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Autonomous Industrial Perception, Monitoring, and Optimisation Using an Agentic Framework with Large Language Models School of Computer Science PhD Research Project Directly Funded Students
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relevant Cluster Lead a 1-page expression of interest indicating the type of long-term fellowship they would like to apply to; how their research programme would enhance the activities in the School
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the air conditions in underground stations. The proposed research will be carried out with the use of Computational Fluid Dynamics (CFD) to simulate the underground station environment with the possibility