12 algorithm-development-"DIFFER"-"Helmholtz-Zentrum-Geesthacht" PhD positions in United Kingdom
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reducing the environmental impacts of computational science. The tools and frameworks developed and maintained by the group are used internationally and include the popular Green Algorithms online calculator
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human
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Award summary This studentship provides an annual living allowance (stipend) of £21,470, and full tuition fees (Home fee level only). Overview This project will develop uncertainty quantification
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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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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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leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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. This project seeks to advance energy autonomy by optimising power conversion, storage, and distribution in such systems, enabling broader adoption in real-world applications. The project aims to develop a PMC
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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