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on utilising an established probiotic to investigate its potential for reducing depressive symptomology and improving cognitive function, whilst exploring possibly underlying mechanisms. Why Join This Project
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degree in Electrical Engineering, Biomedical Engineering, Mechanical Engineering, or a related field, with a genuine interest in Wearable Technology, Sensor Development, Biomedical Devices, and Data
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schemes prioritised over others? What mechanisms exist to evaluate whether schemes are successful (or not)? What lessons can be learned from existing schemes locally and elsewhere to improve outcomes
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and culture within it. Who we are looking for We are looking for an enthusiastic, self-motivated candidate, with a 1st or high 2:1 degree in computer science or mechanical engineering. The candidate
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). Related projects include: UK Hub for Quantum-Enabled Position, Navigation and Timing (QEPNT) –Glasgow led. EPSRC Programme Grant– Chip-Scale Atomic Systems for a Quantum Navigator. STFC-NSERC UK-Canada
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second) in maths, physics, computer science, electrical and electronic engineering, mechanical engineering or social science disciplines looking for a challenging research project in an interdisciplinary
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second) in maths, physics, computer science, electrical and electronic engineering, mechanical engineering or social science disciplines looking for a challenging research project in an interdisciplinary
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. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
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or international students) fully funded. Competitive Tax-free annual stipend for 3 years. Requirements The candidate should have a 1st class degree (BEng or MEng/MSc) in electrical/mechanical
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have a 1st class degree (BEng or MEng/MSc) in electrical/mechanical engineering. Expertise in numerical tools (Ansys, JMAG, .etc) and programming are desirable. Experience in electrical machine