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-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research
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6. Desirable criteria Evidence of active collaboration with dry lab and co-development of algorithm for the prediction of epitopes. Downloading a copy of our Job Description Full details of the role
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, the appointed candidate will work closely with the line manager to develop novel control algorithms in EAP soft robotics combining Gaussian Predictors, hands-on laboratory experiments and JULIA computing
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their own research programme as well as support in the delivery of AI upskilling and expertise transfer to government. What you would be doing The objectives for the role are: to deliver ground-breaking
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims
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the US National Science Foundation. The overall aim of this project is to determine the geometry and plumbing of the hydrothermal system of an active submarine arc volcano and its relationship with
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-fostering prototypes through co-design with children, creating evaluation frameworks to assess children's algorithmic agency in educational contexts, and establishing design principles that prioritize
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. You will focus on developing microwave techniques and associated electronics to precisely control the curing process, using AI-based algorithms to optimise outcomes. Full support will be provided
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Faculty/Directorate Faculty of Engineering, Computing & the Environment School/Function Faculty General (ECE) Contract type Fixed Term Contract Location of vacancy Penrhyn Road The Role Kingston
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values diversity acting as a role model and fostering an inclusive working culture Person Specification A First degree with strong quantitative and informatics component e.g. biological, biomedical