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United Kingdom Application Deadline 30 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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, ranging from AI to novel geometry modelling techniques. While this position is initially for a fixed term period, depending on the success of the role, opportunities will be explored to extend it beyond
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an exciting opportunity to contribute to a high-impact project focused on flexibility modelling and grid impact optimisation, supporting the transition to sustainable cities and societies. You will be
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the development of new anchoring technologies for floating offshore wind turbines. Your role will focus primarily on the development of simplified computational models, and you will contribute
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To contribute to the development and integration of AI models in real-world systems with a geospatial component Essential Criteria PhD in a relevant environmental/ecological science or Computer Science or a
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on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from
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University of Surrey. You will be working on a 3-year project (funded by Innovate UK) looking at coupling of fluid and solid models and how we could use AI to speed up the computational time required. You will
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Framework Programme? Not funded by a EU programme Reference Number 5151 Is the Job related to staff position within a Research Infrastructure? No Offer Description Faculty of Engineering and Applied Sciences
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an enterprising spirit. The School runs a full range of programmes including undergraduate, postgraduate and MBA programmes, as well as a PhD programme. The School works closely with a network of business partners
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. The partnership project combines digital agriculture, crop modelling and molecular breeding to improve the resilience and sustainability of the East African tea crop. You will have the opportunity to visit data