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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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areas: formal methods, machine learning. Additional knowledge and experience within the following areas are appreciated: formal verification, deep learning, dynamical systems, control theory. Excellent
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of a novel condition monitoring system. Responsibilities: Develop machine learning algorithms and methods for condition monitoring of a disruptive new main bearing technology. Analyze sensor data from
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development, employing machine learning techniques applied to industrial datasets, and in collaboration with industry. The applicant will work in the Software Engineering Theme, which is one of several Themes
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critically explore the governmental, technical, and ethical challenges that arise from these pressing developments. AIM focuses on generative AI and machine learning for mental health, with a particular
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critically explore the governmental, technical, and ethical challenges that arise from these pressing developments. AIM focuses on generative AI and machine learning for mental health, with a particular
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new analytical and machine learning tools that will help to interpret complex multimodal data, and you will do it in collaboration with top research scientist in the field and in an international
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and Complexity ; Machine Learning ; Natural Language Processing ; Software, Data, People, and Society ; Programming Languages and Theory of Computing ; Artificial Intelligence; and Image Analysis
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Research, University of Copenhagen, Denmark. Candidates should have a strong background in (genetic) epidemiology, bioinformatics, statistical genetics, data science, machine learning, or a closely related
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working in DTU Electro, in the new Photonic Integrated Circuit based Systems (PICSys) group. PhD scholarship in Machine Learning Techniques for Spectral Shaping of Ultra-Broadband Optical Frequency Combs