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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
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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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will lead efforts to apply state-of-the-art AI techniques (machine learning, deep learning, generative models, etc.) to the discovery and development of new materials in critical domains: water, energy