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Field
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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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the UK require step-change performance of electrical motors from a state-of-the-art continuous power density of 2-5 kW/kg to 10-25 kW/kg by 2035. Incremental improvements in electrical machines built from
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, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art electric motor manufacturing platforms at both locations. Project Description
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, aiming at improving the information extraction and the of icy planetary bodies subsurface by radar sounder data processing. The selected candidate will be expected to develop novel machine learning methods
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, electrical engineering, machine learning, data science, computer science, applied mathematics or in a similar field, or have completed at least 240 credits in higher education with at least 60 credits at
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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Engineering or related fields. Be enrolled in a doctoral program in the scientific area of Electrical Engineering. Willingness to work in the developing of signal processing and machine learning algorithms
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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of mathematics, physics, electrical engineering and AI, helping to develop a theory that explains how and why these systems work — and how to design better ones. Why apply for this PhD? Work on the next-generation
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segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning