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details. Familiarity with electrical and power systems, energy storage systems, EV charging systems, test & measurement instruments, sensors/sensor networks, and PLC systems is preferred. Prior experience
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efficient algorithms with provable statistical guarantees, using tools from: high-dimensional statistics, optimization, probability theory, etc. These positions would be especially relevant for those with a
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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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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toxic gases emissions of various types of batteries potentially deployed in eHC through reduced scale testing. Lastly, you will also need to evaluate the various gas monitoring sensors performance in
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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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-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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candidate will focus on the design, development, and integration of innovative sensors, actuators, and flexible electronic circuits tailored for wearable health monitoring devices. This role will involve
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carbon calculation tool, including data ingestion, estimation algorithms, and automated reporting. o Build and maintain ontologies/knowledge graphs mapping activities, materials, equipment, emission