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dynamical systems. Designing learning-based event-triggered optimal control algorithms to achieve prescribed-time optimal output regulation for uncertain multi-agent systems. Investigating learning-based
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distributed energy resources (DERs). Design & develop optimization algorithms/tools to plan the deployment of DERs such as energy storage systems (ESS), photovoltaic generations (PV), electric vehicle charging
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the regulation of emerging technologies, the societal impact of algorithmic systems, ethics and governance of AI, and the evolving role of media institutions in the digital age. The successful candidate will join
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, peripherals for DSP Applications, design and development tools for DSP, introduction to VLSI, algorithms and architecture for VLSI. The successful candidate should have extensive relevant industry experience
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-forming converters and control algorithms for next-generation renewable and energy storage systems. The role will focus on control design, simulation, and experimental validation to support system stability
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field Strong background in control theory, optimisation-based algorithms and/or machine learning Excellent verbal and written communication skills Proficiency in programming languages in Python and/or C/C
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leverage their expertise to develop innovative algorithms for data analysis. Additionally, they will be responsible for communicating their findings to the scientific community through academic meetings and
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digital energy system modeling, intelligent optimization, and collaborative control. Develop innovative AI models/algorithms, and research key areas such as energy generation prediction, grid and load
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems