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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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research environment with close interaction between experimental and theoretical activities. You will join a thriving community of researchers and benefit from a strong network of international collaborators
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communication skills and willingness to work independently as well as to collaborate with your colleagues and peers. Commitment to complete the PhD coursework (30 ECTS points) and contribute to teaching
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communication skills in both spoken and written English, and experience in working independently and in a team. – see comment We offer DTU is a leading technical university globally recognized for the excellence
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with strong interconnection between experimental and theoretical physics. We provide a large network of collaborators to develop ideas and new projects. Excellent experimental research infrastructure
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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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. 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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for individuals, communities, and cultures. Our work culture is founded on openness, curiosity, transparency, and involvement. Come join us! Read more about the department here; Welcome to the Department
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fellowship in Experimental Quantum Physics . The project is part of the research project “Hybrid Quantum Networks”, which is financed by the Danish National Research Foundation. Start date is (expected to be
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group specializes in experimental quantum technologies spanning quantum information processing, quantum communication, and precision sensing. In this PhD project, you will work at the forefront of solid