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Field
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image analysis, data processing and machine-learning-based modelling. More about the position The main purpose of the fellowship is research training leading to the successful completion of a PhD degree
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the start of the position Have practical experience in machine learning with Python, including training neural networks in PyTorch or a similar framework Have a solid background in signals and systems as
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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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16th October 2026 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD Candidate in Hybrid Learning-Control Methods for Autonomous Underwater
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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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doctoral degree Collect, structure and assess relevant sensor, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results
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machine learning and advanced analytical approaches Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Show curiosity and a strong motivation for the subject
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» Mechanical engineering Engineering » Industrial engineering Computer science » Cybernetics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 Oct 2026 - 23:59 (Europe
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degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning
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in one or more of the following: railway engineering, infrastructure maintenance, rolling stock, condition monitoring, sensor data analysis, statistical modelling, machine learning, life-cycle cost