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Field
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research active academic staff, around 20 postdoctoral researchers and around 50 postgraduates, with research groups in Algebra; Analysis; Bubble Dynamics; Combinatorics, Probability and Algorithms
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, applying and validating your algorithms through laboratory experiments. A PhD in Chemistry, Biology, Computer Science, or related fields with a focus on protein engineering. Proficiency in Python programming
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for AI based algorithms. Research experience in these areas will be highly valued. The successful candidate will also contribute to the formulation and submission of research publications, development
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ecosystems. Fairness, safety, interpretability and trust in AI algorithms and AI-based technologies. Applications with healthcare, energy, sustainability, business analytics, etc. Candidates are expected
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areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
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, algorithm development, AI, and machine learning, supporting fusion energy engineering through advanced simulation and workflow optimisation. You will work on impactful, collaborative projects across academia
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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with the regulatory environment around Deep Learning or Machine Learning algorithms Experience applying quality system standards, software development standards and regulation, e.g. ISO 13485, IEC 62304