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experience with finite element or finite difference modelling and its underlying fundamentals. The student should be able to program in MATALB or Python (MATLAB preferred as lead supervisor is more familiar
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modelling of the atmosphere using python and FORTRAN, optimising momentum and flux balances using AI tools. This training will equip them for a career in atmospheric science.
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
. Proficiency in programming and modern ML tooling (e.g., Python, PyTorch); experience with LLMs (e.g., using Hugging Face libraries) is a plus. Ability to reason about complex systems and turn ideas into code
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. This research is ideally suited to candidates with interests in photonics, metamaterials, ultrafast optics, nanofabrication, and computational electromagnetism. Strong coding (Python /MATLAB) and experimental