29 wireless-sensor-networks PhD positions at Technical University of Denmark in Denmark
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failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics
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highly cross-disciplinary research section with over 50 members that focuses on the development of micro and nanotechnology-based sensors, detection systems, drug-delivery devices, and energy materials
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, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics, acoustics, power electronics, robotics, and autonomous systems. Technology for people
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. Responsibilities: Conduct research in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function
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Job Description We are seeking candidates for a 3-year PhD project as part of the European Marie Skłodowska-Curie Actions Doctoral Network on “Consumer Energy Demand Flexibility in Electricity Use
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learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as
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, encouraging creativity, diversity, and teamwork. You will have great opportunity to build strong networks with internationally renowned researchers at DTU and other universities as well as industrial partners
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loads — EV fleets, residential batteries, smart heat pumps, and data-center clusters — across distribution and transmission networks is critical to unlocking deep decarbonization and maintaining grid
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. Opportunities to participate in conferences, symposia, and networking events to share and enhance your research. Your role will be pivotal in driving AI innovation and contributing to a transformative approach to
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key