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or international equivalent in a subject relevant to the proposed PhD project (electrical engineering, electronics engineering or computer engineering). Enthusiasm for research, the ability to think and work
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an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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computer literacy, good inter-personal communications skills. Desirable skills: A Master in Health Economics with experience in cost effective analyses. Funding notes The three year studentship covers UK
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, distributed ledgers) Desirable: Experience with generative AI (e.g. LLMs) Interest in Human-Computer Interaction Interest in privacy enhancing technologies (PETs) Other: Experience in presenting or preparing
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. Essential skills: A BSc degree or equivalent ideally in a health related field, excellent computer literacy, good inter-personal communications skills. Desirable skills: Previous experience in medical
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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suitable for a hard-working researcher with an interest in respiratory infections. Essential skills: A BSc degree or equivalent ideally in a health related field, excellent computer literacy, good inter
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fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme! How does it work? Candidates will study for a four year, full time funded PhD
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techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference