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An Information Theoretic Approach to the Smart Grid School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Inaki Esnaola Application Deadline: Applications accepted all
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partners from 11 countries. The project aims to develop a simulation environment that optimally locates EV charging stations and integrates them into the power grid using advanced AI models. It will
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theoretical frameworks, experimental setups and theoretical proofs. Testbed Development and Management: Oversee the development, management and utilization of lab testbeds, including smart grid co
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theoretical frameworks, experimental setups and theoretical proofs. Testbed Development and Management: Oversee the development, management and utilization of lab testbeds, including smart grid co
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new generation of embedded intelligence within grid-connected power converters and variable-frequency motor drives with edge computing and machine learning capabilities. The Postdoc positions are part
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systems, renewable and nuclear energy systems, energy storage, buildings and district energy systems, smart-grid applications, process systems engineering, advanced control, and artificial intelligence
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-side assets and the microgrids they form: e.g., residential and small commercial connections, PV, batteries, EV charging, heat pumps and flexible demand with grid-forming (GFM) and grid-following (GFL
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applications, including smart cities and residential self-consumption, backup power in isolated areas, energy management systems, and grid balancing for renewable power integration. A key line of work
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Generative-AI-powered Energy Digital Spine, i.e. a digital backbone turning today’s fragmented digital energy sector into an interoperable Software-Defined Smart Grid. One of it four pillars is