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Skip to main content. Profile Sign Out View More Jobs PhD scholarship in Machine Learning in IoT Edge Devices – DTU Electro Kgs. Lyngby, Denmark Job Description We invite applications for a PhD
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of specific 2D functionalization strategies, we have a broad and flexible focus when it comes to the employed methodologies (empirical models, DFT, many-body perturbation theory, machine-learning) the target
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Dynamics (AIMD), and Machine Learning (ML) tools to develop theoretical electrocatalytic frameworks. Hereby, the project will challenge existing research on catalysis, move the boundary of fundamental
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with researchers from machine learning and fire safety and material science in a truly interdisciplinary environment. Co-author scientific papers aimed at high-impact journals. Participate in
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central goal of the project is to develop explainable machine learning models that allow insight into the interaction between genetics and other types of data that can be used for developing tailored
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with relevant data collected at industrial scale Apply AI / machine learning to support on-line deployment of the mathematical model for real-time prediction Using the model, investigate and prioritize
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event-based simulation and a variety of machine learning techniques. A strong curiosity and interest in current and future mobility challenges, especially those related to logistics. Excellent written and
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to a Master’s degree. The successful candidate has an education in a relevant engineering field or computer/data science and is motivated to specialize in electrical power systems. Candidates
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microscopy and automated data analysis via machine learning we aim at creating structure-functionality correlations for tailored materials. In collaboration with theoreticians, we aim at extracting data from
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assessments. The exceptional availability of Danish Big Dataset on health outcomes, consumption and sustainability. A unique combination of mass balance-based Source-to-Impact Models and Machine Learning