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degree in Computer Science, Math, Statistics or Engineering programs. Ph.D would be an asset. Knowledge of classification algorithms, vision-language models (VLM) and Hugging Face Transformers is an asset
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: Starting October 2025, we require an enthusiastic graduate with a 1st class degree in engineering, maths or a relevant discipline, preferably at Masters level (in exceptional circumstances a 2:1 degree can
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to applicants with a first-class or upper second-class degree (or equivalent) in any Engineering discipline, Physics or Maths. Please apply by submitting your CV, cover letter and transcript to
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Physics, Theoretical physics, Maths or other closely related subject (e.g. Materials Science with evidence of strong computational background and skills). To apply, please contact the supervisors; Dr Thomas
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inference of diffusion models and LLMs. The applicant should have a strong background in machine learning and maths.
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maths, physics, mechanical or materials engineering or a closely related discipline is essential. A Masters-level degree or publication record in any of the above fields would be advantageous. Good
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. Experience working with liquid AFM and lipid nanoparticles is preferred. Math or programming experience preferred. Candidate should have a background in physics, engineering, or chemistry. Experience working
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The project also has a sub aim of protecting the heart from radiation during radiation therapy treatments for cancer patients. The project would suit someone with a strong maths, physics or
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systems. Advanced programming skills. Academic writing skills and the ability to typeset papers in LaTeX. It is highly meritorious if you enjoy working with math notation and formal proofs. Alternatively
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characterised by the property that all critical points belong to attracting basins. Bergweiler, Fagella and Rempe ([BFR], Comm. Math. Helvetici 2015) studied bounded Fatou components of hyperbolic transcendental