103 electronics-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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, engineering, mathematics, physics or related field; strong programming skills; interest in ML, computer vision, robotics, embodied AI or autonomous systems; motivation for independent research and high-quality
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About the project: Machine learning accelerated electronic transport calculations for complex materials Supervisor: Prof. Neophytos Neophytou, University of Warwick Advancements in materials
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
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process or the PhD in Oncology should be directed to the Postgraduate Administration Team, Department of Oncology (email: [email protected] ) Enquiries relating to the Richard Elphick
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, sensor design and calibration, finite element modelling, polymer processing, embedded electronics, and ex vivo tissue methods. The University is uniquely positioned to benefit any applicant interested in a
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combine practical electronics and experimental prototyping with physiological modelling and computational analysis. The central scientific objective is to establish how much information about cardiovascular
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real