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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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Algorithms Agile Programming Applied Machine Learning Computer Graphics Web Design and Programming Cloud Technologies Advanced Databases and Big Data Introduction to AI Natural Language Processing DevOps Data
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a much stronger focus on experiential learning. This includes significant amounts of small group task-based working and research projects as part of the curriculum. About you You will: Possess
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, the adoption of AI in project management remains in the nascent stages. This PhD project will critically investigate how AI is shaping the management of projects and project studies. It aims to generate new
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to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models. Your experience in real-time implementation of federated AI and Edge-based
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theoretical physics, quantum computing, or machine learning and have completed or be in the final stages of a PhD in this or a related discipline. Main duties and responsibilities Design and analyse quantum
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of publications. Criteria Essential or desirable Stage(s) assessed at A PhD (or close to completion of a PhD) in Machine Learning or a similar area (e.g. in Computer Science, Electrical and Electronic Engineering
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. program and will work on the development and analysis of statistical methods for machine learning, particularly in the context of high-dimensional models and with a particular focus on methods such as
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Subject areas of particular interest may include: Machine and Deep Learning Data Visualisation Generative AI and LLMs Programming (Python, R, SQL) Databases and IoT Applied Statistical Methods You will also
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a range of methods including causal inference, signal processing and machine learning to investigate whether and how acceleration data from flying birds can be used to characterise changing levels