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design, development, and validation of material, control systems, and algorithms for next-generation soft haptic actuators and experiences. Note that the research involves significant interactions with
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with data science, and data management (including IoT, Edge, and HPC). This is a fully funded position for 12 months within the HEXAPIC project, conducted in collaboration with Prof. Leon Kos' team
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and physics-based models for battery behaviour. Designing advanced BMS algorithms for real-time monitoring and control. Creating generalizable tools applicable to various battery types, geometries, and
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collaboration with Prof. Giovanna Tinetti and her team and collaborators at KCL. The main purpose of this role is to develop new and/or to use existing models to simulate the atmospheres of exoplanets and use
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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algorithms for the scanner using machine learning and deep learning. Qualifications: The position requires some background in machine learning, optimization, and deep learning. Some familiarity with MR physics
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About the role We have an exciting opportunity to join the Rare Genetic Disorders Research Group led by Prof. Stephan Sanders in the Department of Paediatrics at the University of Oxford as a Full
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the direction of Prof Hamid Bouchachia . The project will develop Scenario-driven and opportunistic machine learning with the aim to: design and develop AutoML mechanisms for performing scenario-based algorithm
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now Are you interested in designing and analysing algorithms for Bayesian networks that contribute to traceability, source attribution, and justification in clinical decision support systems? Do you
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Digital twin railway network School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof D Fletcher, Prof R Harrison Application Deadline: Applications accepted all