63 condition-monitoring-machine-learning Fellowship positions at UNIVERSITY OF SOUTHAMPTON in Uk
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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calculations; Experience with developing, training, and optimizing neural networks or other machine learning models. For this position we are targeting a salary corresponding to Level 4 Spine Point 28 - 30
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Processing and Control Unit, targeting compact and robust power delivery. Developing a PASTA testbed enabling remote control and monitoring. Optimising a PASTA plasma reactor. Developing a PASTA pilot test
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implementing bioinformatics pipelines from raw data, applying a range of advanced statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead
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Researcher (R3) Country United Kingdom Application Deadline 26 Oct 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
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(R1) Established Researcher (R3) Country United Kingdom Application Deadline 1 Dec 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework
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with multiple long-term conditions in the community within our Long-Term Conditions theme. In this programme of work, we will enhance health and well-being of people living with multiple long-term
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) Established Researcher (R3) Country United Kingdom Application Deadline 6 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
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System and beyond. The new climate model, OASIS, aims to overcome the limitations of current frameworks and enable unprecedented simulations of planetary environments under a wide range of conditions. The