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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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of Civil and Mechanical Engineering, Thermal Energy Section. We look for a talented, self-motivated, and team-oriented individual who thrives in a collaborative environment and enjoys tackling complex topics
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on developing machine-learning-based or statistical emulators to approximate key outputs of complex Earth System Models, with the aim of enabling efficient uncertainty quantification, sensitivity analysis, and
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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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opportunities to engage with top universities and leading cybersecurity researchers, expanding your professional network. Traditional cybersecurity relies on strong defensive barriers, but sophisticated attacks
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, networking, cybersecurity, or a related area relevant for the position. Solid knowledge of system security. Experience with real-time systems and/or safety-critical systems is a plus. Proven research skills
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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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documentation. Excellent communication skills in English; with understanding of Danish or a willingness to learn. A strong independent working style and the ability to plan and execute complex tasks effectively
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interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and
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, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks