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, and smart grids. This high level of knowledge transfer is achieved through both competitive research projects and direct contracted research. Therefore, public and private entities have access to a pool
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: Renewable energy systems (solar, wind, smart grids) Advanced digital technologies (Industry 4.0, IoT, automation) Green process engineering (circular-economy and low-impact methods) GTI’s mission is to train
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advanced operational technology testbeds within the Duke Energy Smart Grid Laboratory. The candidate is expected to develop a funded research program that complements and supports the multidisciplinary power
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reinforcement learning, agent-based modelling and simulation, autonomous decision-making, communication protocols, and applications in robotics, smart grids, and social networks. We consider computer science
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academia and industry by addressing research on daily life issues, such as healthcare, space, mobility, human language technologies, agri-food, industry 4.0, and smart grids. This high level of knowledge
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to the specific local positive energy districts (PEDs). A PED generates more renewable energy than it consumes. But creating such a district is not just a technological task. It requires smart collaboration between
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communication capabilities for real-time monitoring of critical infrastructure. A key application area will be smart grid powerline monitoring, where you will work on: Designing and leading the development of low
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academia and industry by addressing research on daily life issues, such as healthcare, space, mobility, human language technologies, agri-food, industry 4.0, and smart grids. This high level of knowledge
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promotes cooperation between academia and industry by addressing research on daily life issues, such as healthcare, space, mobility, human language technologies, agri-food, industry 4.0, and smart grids
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Optimization of energy system design with application of smart sensors and controls, integration of renewable energy sources and heat pumps Application of artificial intelligence/machine learning for real-time