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on identifying and discovering patient sub-cohorts within an electronic health record database. This discovery process will take place via the design of deep clustering algorithms based on state-of-the-art
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capable of supporting and collaborating with humans in complex, real-world settings. You will be responsible for researching and developing novel algorithms and techniques to achieve the project’s
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solvers • Implementing and analysing online optimisation algorithms for real-time grid balancing • Liaising with project partners (NESO and University of Strathclyde) Essential selection
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for real-time human data processing in interactive settings. Technical expertise in areas such as electrophysiological recording, VR paradigm design, closed-loop algorithm development, or clinical
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Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g
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scheduled for the end of May/early June.
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on Monday 11 May 2026. Interviews are likely to be scheduled for the end of May/early June.
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characterisation, including instrument control, experiment scheduling and monitoring, and standardised, reusable pipelines for NTCF users. They will analyse OPV materials and device data, contribute to the design of
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characterisation, including instrument control, experiment scheduling and monitoring, and standardised, reusable pipelines for NTCF users. They will analyse OPV materials and device data, contribute to the design of
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closing date for applications is 12.00 noon on Tuesday 21 April 2026, interviews are likely to be scheduled for late April 2026. Applications for this vacancy are to be made online via our e-recruitment