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EPSRC ReNU+ CDT PhD Studentship: Physics-informed machine learning for deep geothermal systems under uncertainty. Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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for all. This PhD project aims to develop, physics-informed surrogate models to support the design and optimisation of deep geothermal energy systems under subsurface uncertainty. Focusing initially
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PhD in Electrical and Electronic Engineering: Advanced Protection Strategies for Low Voltage Networks with High Penetration of Power Electronic Devices (Prot-LVNet) Award Summary 100% fees covered
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PhD studentship in Computer Science: From Formal Requirements to Specification-based Automated Testing for Safety-Critical Medical Device Software Certification Award Summary 100% fees covered, and
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Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,835 (2025/26 UKRI rate). Overview The Award This PhD project is a partnership between Scottish Power and
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that respects both user preferences and legal requirements. Your research will create it. What makes this different This isn't a typical PhD. Through structured industrial secondments (several weeks yearly
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. This PhD project explores how formal requirements and automated testing can produce clear, traceable, regulator-ready evidence for medical device software certification. Part of the international PlaTFoRm
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external control. Autonomous agents that can perceive, reason, plan, act, and learn, together with self-configuring, self-healing, and self-optimising behaviours, provide the foundational principles
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, which are often limited and costly. This project explores the challenges of deploying AI at the edge within the context of federated learning (FL). Topics of interest include, but are not limited