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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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. Process Intensification strategies will be employed to offering substantial advantages in energy efficiency and process economics. The impact of key operating parameters on hydrothermal processing will be
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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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Eligibility criteria We are adopting a contextual admissions process. This means we will consider other key competencies and experience alongside your academic qualifications. An example can be found here . A
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. Overview This PhD project is part of the EPSRC Centre for Doctoral Training in Process Industries: Net Zero (PINZ) . The PINZ CDT will train the next generation of process and chemical engineers, and
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Resilience of Cyber-Physical Transport Systems Award Summary 100% fees covered and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate) (Only available for UK students
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twins, enabling real-time stress-testing under simulated edge cases like cyber-physical attacks and sensor failures. The Research Challenges There exists a complex interplay of factors that present
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, finite element simulations of vibration propagation, and AI-based signal analysis to establish a physics-informed understanding of the relationship between structural behaviour and fall detection accuracy
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motivated PhD candidate with interests and skills in computational modelling and simulations, fluid dynamics, mechanical engineering, physics and applied mathematics. You should have experience in one or more
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into volatile chlorides. The process efficiency, however, depends on a complex interplay of particle-scale interactions and particle/solid body interactions. Current industrial practice relies heavily