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Electric drives are essential to the UK's net-zero ambitions and will play a crucial role in powering the electric revolution. Future electric drives must be smarter, more power dense, and more efficient
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Newcastle University through the Industrial Doctoral Landscape Award (IDLA). The project runs alongside the £8m D-Suite Beta Strategic Innovation Fund project. As an IDLA PhD student, you will be offered
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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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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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their operational reliability. The PhD student will combine mathematical models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify
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research, industry, and medical engineering. Methodology The overall aim of the project is to design and evaluate processes that integrate formal requirements and formal specification-based automated test
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for tuition and the charge made by the university for tuition fees for international students. A minimum of a 2:1 honours degree and a Masters degree at Merit level, or international equivalent, in a related
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, an interest in biomedical research, and the ability to work independently are essential. Prior experience/knowledge in Electromagnetism, COMSOL, microfabrication, and PCB design are desirable but not essential
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increased completion time, training loss, energy consumption and reliability. To tackle communication overhead in FL and SFL, we will focus on designing protocols that minimize data exchange between devices
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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