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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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to use complex computer code e.g., TRACK algorithm · Difficulty to grasp analytic techniques used outside of academia and used within the insurance industry · Difficulty in complex
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms
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to modern-day technologies and the net-zero transition. Despite their strategic importance, the UK remains heavily dependent on fragile global supply chains and primary mining routes with significant
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This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further
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safer, more reliable, and more sustainable renewable energy systems. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods
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, predictive modelling, and autonomous maintenance solutions. You thrive on scientific discovery, enjoy tackling complex multi-physics problems, and have the drive to explore innovative concepts
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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, transportation networks, and water treatment plants, increasingly rely on AI-driven decision-making for efficiency and autonomy. However, these systems face unique safety challenges. Real-world conditions
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and BBSRC Centre for Doctoral Training in Negative Emission Technologies for Net Zero (CDT in Net2 Zero) is an equal partnership between Aston University (lead), University of Nottingham, Queen’s