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Job Description Are you passionate about leveraging IoT, machine learning, and optimization to make energy districts and communities more sustainable? We are looking for a highly motivated and
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candidates with expertise in one or more of the following specialized areas: Machine Learning / Deep Learning Uncertainty Quantification Wind Farm Flow Modelling Wind Farm Control Wind Farm Design Wind Farm
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Embedded AI, Edge AI, TinyML, and AIoT, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system
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Communication, Singal Processing, Low Power Electronics, Wireless Sensing, Low-Power System Design, Machine Learning & Edge Inference, Underwater acoustic communication. Furthermore, you have a proven record of
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qualification, you must hold a PhD degree (or equivalent) in computer science, computer engineering, or electrical engineering. Hardware design in a hardware description language such as Chisel, VDHL, or Verilog
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and may commence preferably in the fall of 2025. Your tasks As a PhD student at DTU, your work will include: Learn to perform rigorous and relevant research Collaborate with key academic and/or
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for computational biology and a track record of excellence in graph machine learning and multi-omics data integration? Look no further – an exciting Postdoc opportunity awaits you at the Novo Nordisk
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journals and conferences in your field. Secure funding for your research area from both Denmark and the European Union. Teach, guide, and supervise BSc and MSc students, as well as supervise PhD students