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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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learning, control theory, and embodied autonomous systems. The successful candidate will contribute to the development of learning-based control methods that are not only high-performing, but also safe
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Start date: 28/09/2026 Funding Sponsored by Cranfield University, this full-time PhD Studentship will cover the PhD programme fees and provide an annual bursary of £20,780 (tax free) for three years
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Materials, mastering scientific machine learning, uncertainty quantification, and high-performance computing. Your models will inform fusion design and advance AI-for-materials. Perfect for physics, maths
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On Brain Disorders. The successful candidate will join us (fixed-term 3 years, 100% FTE contract of employment) to develop computational models of the neonatal brain, working on the intersection
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Coastal communities face increasing pressure from sea level rise and habitat loss. Seawalls and rock armour provide essential protection but have high embodied carbon costs and often degrade local
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, limited predictability and slow process optimisation. The PhD sits within an interdisciplinary research environment that combines laboratory experimentation with mechanistic and computational modelling
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tax-free stipend for Home and Overseas students of £25,726 per year. Applications Close: 28th February 2026 Start Date: 28th September 2026 (contract duration 4 years) Railway performance across the UK
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software package VAMPIRE (vampire.york.ac.uk ). The successful candidate will benefit from strong national and international collaborations throughout the project and will develop skills in high-performance
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-performance computing, and direct interactions with experts in AI for Science, computational imaging, and large-scale experimental methods (with Prof. Jeyan Thiyagalingam). You will spend time at both sites (75