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confined battery geometries. Advanced modelling—including computational fluid dynamics (CFD) and transient thermal analysis—is required to accurately capture heat flux distributions, temperature uniformity
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algorithms * Parallel algorithms and distributed computing * Parameterized complexity and structural graph theory * Random structures and randomized algorithms * Sublinear and streaming algorithms
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of next generation agentic AI systems. In this PhD programme, you will redefine how the world works, learns, and discovers, turning bold ideas into tools used by millions. You will then become one
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different VME types. Build predictive spatial models for VME distribution and resilience under future climate scenarios. Training The candidate will gain skills in: Deep-sea survey techniques using remotely
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architected materials or metamaterials (MTM) that can undergo targeted non-linear response. You will develop a computational framework that can reveal novel Multiphysics (thermo-mechanical) MTM solutions
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associated with genome dominance. Transposable element distribution and activity will also be analysed using long-read sequencing. This project is primarily computational and offers comprehensive
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King's College London Department of Engineering | London, England | United Kingdom | about 2 months ago
through the EU Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description About the Project This PhD project in fire
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technologies (LCTs, such as solar PV, electric vehicles, heat pumps or energy storage) to distribution networks. New active technologies that provide flexibility, such as network operator-owned power electronic
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interact without full trust in a centralized party. This research project aims to develop cryptographic solutions and frameworks that secure AI systems, particularly in decentralized and distributed settings
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. road, power distribution). This project will develop new insights into what drives flooding and extreme wind to co-occur on timescales from (sub-)daily to seasonal. Our recent work suggests that