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data, which often only provides snapshots in time, neglect coastal waters, and overlook certain species. You will work on integrating diverse data sources to overcome these limitations and develop a more
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. Across these applications areas, a central and critical challenge in bioprinting is the design of appropriate biomaterials to serve as synthetic matrices and scaffold for the tissues. You will be part of a
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or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, geophysics and acoustic data modeling. A strong experience with software defined radio Automatic
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to work in an international team, work with forefront methodologies, and contribute to the investigation of persistent, mobile and toxic substances in drinking water, then DTU and Norwegian University
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. An external research stay of approximately 3 months in a collaborating institution is a mandatory part of this PhD fellowship position. You will work in close collaboration with experienced researchers and
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ecosystem, we encourage innovation and entrepreneurship. We have a strong ethical, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility
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limited. We are offering a PhD scholarship for a student to develop ambitious new machine learning strategies for generating AI-ready data. You will work at the frontier of active learning and ML-guided
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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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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the European Innovation Council (EIC), you will work in close collaboration with researchers at DTU, Cambridge University, Technical University Munich, Imperial College London and the company SolarFoods. You