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An exciting opportunity has arisen for a talented computer scientist to join our team as a researcher within the Green Algorithms Initiative in the Department of Public Health and Primary Care, one
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different neurons in the neocortical circuit and use them for closed-loop brain stimulation. By examining how these spatiotemporal dynamics relate to behaviour, you will develop new brain decoding methods and
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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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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may decide to implement a same format slightly differently, leading to irreproducible results, non-portable code, hard-to-find bugs, and other unexpected behaviours. On the backdrop of this complex
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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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technologies. Agricultural applications present a unique opportunity for AI systems as they often involve repeatable tasks within a relatively low-safety-risk environment, unlike public or transportation
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design that is applicable to different types of energy harvesters. This project will explore how PMC can be optimised to maximise efficiency, reliability and scalability using novel circuit topologies
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty