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Open to: UK fee eligible applicants only Funding providers: EPSRC and LaVision UK Ltd Subject areas: Biomedical Engineering, Experimental Mechanics, Image-Based Measurements, Biomechanics Project start dates: 1 October 2025 (Enrolment open from mid-September) Supervisors: Hari Arora (Biomedical...
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs) into reliable and efficient decisions. The work spans
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nonlinear control and optimisation to develop novel, bio-inspired neural networks that flexibly and robustly control locomotion in multi-limbed robots. "Self-organised clocks for reliable spiking computation
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solutions, focusing on integrity assurance for safety-critical applications. It addresses the growing need for cognitive navigation systems that are able to operate reliably with a diversity of dedicated
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to develop novel, bio-inspired neural networks that flexibly and robustly control locomotion in multi-limbed robots. "Self-organised clocks for reliable spiking computation" (Supervisor: Prof Timothy O'Leary
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device will require an easily operable and user-friendly detection technique to identify targets of interest with minimal training or specialized equipment. The goal is to enable rapid, reliable results
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playthrough into partial game logs and link them to the final result. This gives us grounded, reliable feedback: we know which sequences of actions led to a win or a loss. From these partial logs, we learn a
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T3 (Applications) through reliable quantum advantage assessment. Project Description The project addresses the critical need for reliable, scalable verification and benchmarking schemes in quantum
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, unit reliability analysis, and shared variance component analysis (SVCA) Create comprehensive data visualisations and perform statistical analyses to assess stability and plasticity of multisensory