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control algorithms for whole system efficiency optimisation. Design and simulate power management circuits using tools like SPICE or MATLAB/Simulink. Prototype and test circuits with real energy harvesters
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techniques, data analysis with desirable knowledge of material characterisation, rheology and imaging. Scientific interest, dedication to research and career goal to work in physical science research
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Overview This research assistant post will enable the candidate to undertake a PhD on the diagnosis of pancreatic cancer, specifically focusing on examining the use of imaging before a diagnosis is
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imaging data and possess sufficient specialist knowledge in brain imaging. The position is full time (part time considered) fixed term for two years. The closing date for applications is noon on 21 April
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will hold a relevant PhD/Dphil in statistics, machine learning or similar area, together with relevant experience working with brain imaging data and possess sufficient specialist knowledge in brain
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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm
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We are offering a fully-funded PhD studentship working at the interface of photonics, robotics and fusion research. The project is focussed on the development of new imaging technologies to enhance
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the growing demand for sustainable AI-enabled systems, this PhD brings together low-power computing, energy-aware design, and thermal optimisation. You’ll work with advanced profiling tools, prototype long-life
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will lead to natural collaboration opportunities. The primary methods used in this project will be experimental, involving fluid characterisation and high-speed imaging experiments, using Phantom high
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hardware-software prototype for Non-Intrusive Load Monitoring (NILM) that can provide real-time, interpretable energy-saving suggestions to households—completely on-device. This role involves applied machine