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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have documented experience in
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mathematics, optimisation, biomathematics, statistics, and data science with strong connections to engineering, natural sciences, and industry. About the research project We will recruit one postdoc in applied
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development of analytical solutions, data analysis and machine learning. Candidates should have a demonstrated record of scientific publications in international journals and participation in conferences
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engineering, or commensurate job experience with computational techniques including signal processing, statistics, and machine learning. Proficiency with MatLab, Python, R and/or equivalents is essential
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Center for Advanced Systems Understanding, Helmholtz Center Dresden-Rossendorf | Germany | 13 days ago
Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. The Department of Machine Learning for Infection and Disease is looking for a Postdoc (f/m/d) on Generative AI
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radiation beamline hands-on experience in X?ray microscopy techniques, X?ray diffraction or X?ray fluorescence working knowledge of image processing e.g. using machine learning German skills For further
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-scale research facilities (e.g. DESY, ESRF), including coordination and setup of experiments Development of data workflows and analysis strategies (in collaboration with our machine learning team
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 7 days ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
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platforms. Experimental data collection is carried out in close collaboration with other researchers in the project. The responsibilities include, among other things: developing and applying machine learning