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with Python and common AI frameworks (PyTorch, TensorFlow) Interest in hallucination detection, robustness, trustworthiness, and (optionally) explainability for autonomous systems (Optional) Experience
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equivalent in a related discipline. This project would suit someone with: Experience with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates
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data assimilation. • Proficiency in one scientific programming language: MATLAB, Python, or Julia. LanguagesENGLISHLevelGood Research FieldOther Internal Application form(s) needed
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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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now! Education · A PhD in an engineering discipline is mandatory. Experience and skills · Proven programming expertise in Python is necessary; knowledge of other programming languages
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and scripting languages (e.g. python) is a plus. The start date of the studentships is 1 October 2026. For project 1, applications from students from the UK, EU and overseas are welcome. The studentship
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electrical or mechanical engineering Strong mathematical skills Experience in modelling energy systems Very good knowledge and experience in programming (e.g. Python, Matlab, C, C++) Fluent in written and
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machine learning, deep learning, or computer vision Experience with Python and common AI frameworks (PyTorch, TensorFlow) Interest in hallucination detection, robustness, trustworthiness, and (optionally
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. Candidates must have proven ability to work with large datasets, coding with Python/Fortran/C++ and ideally experience with high-performance computing. Applicants from an industry background are encouraged
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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