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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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and AI-based segmentation, with a particular emphasis on inter-vehicle collaboration. The overall goal is to develop robust, scalable methods for detecting and classifying structural anomalies (e.g
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multimodal learning data (e.g., individual as well as collaborative verbal interactions, student gestures, task and activity sequences) and evaluating long-term learning outcomes. The work package is part of a
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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. The positions are fixed for 3 years. PhD Focus Area: Collaborative Control and Localization of Unmanned Systems The multi-vehicle platform will be developed by the project partners during the course of the
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collaborative settings and wish to play a key role in an EU-funded project with researchers from multiple countries? If so, this PhD position could be a good opportunity for you. This project focuses
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the Independent Research Fund Denmark. The project will involve active research collaboration between national Danish partners DTU and DHI, as well as with international partners University College Dublin, Ireland
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machine learning for safe and optimal control of cyber-physical systems. The projects are expected to be funded by the VILLUM INVESTIGATOR project S4OS (“Scalable analysis and synthesis of safe, secure and
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about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its
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meaningful questions, and possess the determination to pursue innovative solutions in a rapidly evolving technological landscape. DTU Wind and Energy Systems, in collaboration with Vestas - the world’s leading