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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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Skip to main content. Profile Sign Out View More Jobs PhD scholarship in Machine Learning Techniques for Spectral Shaping of Ultra-Broadband Optical Frequency Combs - DTU Electro Kgs. Lyngby
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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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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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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
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on cutting edge machine learning methods? If you are establishing a career as a researcher in machine learning, and you are motivated to work with the latest methods for quantifying uncertainty in neural