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Job Description Are you passionate about leveraging IoT, machine learning, and optimization to make buildings smarter and more sustainable? Join us to advance your career by working
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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning
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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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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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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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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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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
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streams—including data streaming to cloud databases, scientific visualization, and integration of machine learning workflows. Development of additional modules within commercial FE software (ABAQUS, MSC
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like