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(a) networked multi-agent human-robotic systems that work collaboratively in a well-coordinated and safe manner, (b) computational design and digital manufacturing of components, (c) design of
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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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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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, 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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. 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
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customization of Large Language Models for educational purposes. The ideal candidate must have a strong professional network and documented collaboration capabilities, alongside a proven track record in
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willingness to collaborate, network, and interact with other researchers, and the ability to disseminate research in seminars, conferences, and teaching classrooms. We expect you to lead and undertake