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Mechanisms for Vehicular Networks Summary of the Scholarship Objectives: The scholarship aims to design, implement, and evaluate optimization solutions for vehicular networks based on Reinforcement Learning
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Job description We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established
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characterization hardware (e.g., source meters, potentiostats, impedance analyzers) and basic signal processing, while interest in machine-learning-based process control is a plus knowledge of fibre-reinforced
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argumentation deontic/normative reasoning decision- and/or game theory reinforcement learning A course the candidate has taken or a project the candidate has completed counts as documented background. Experience
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exposure to one or more of: machine learning, reinforcement learning, robotics/autonomous systems, information theory, or human–machine interaction will be an advantage. Inquiries. Interested candidates
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of reinforcement learning (RL) for industrial process control and optimisation. The research focuses on developing RL methods that can support decision-making and control in complex industrial systems while
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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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communication systems, we encourage you to apply—even if you do not meet every item in the project description. We value candidates who are eager to learn, think creatively, and work across disciplinary
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results. ● Deploy the results developed in the first stage to linear value function estimation problems in reinforcement learning theory. Establish the foundations to generalize the results to non-linear
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) at WMG, University of Warwick to work on: Environmental effects on the long-term mechanical performance of short-fibre-reinforced thermoplastics (ENFORM) Short-fibre reinforced thermoplastics (SFRPs