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Campus Data Science and AI Project-based Teaching and Learning) course model. We seek instructors from industry, government and academia to teach permanent and special topics undergraduate courses
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, or a related field Strong background in computational modelling of surface chemistry and oxide materials Experience in developing and training interatomic machine learning potentials Experience in
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, train, and evaluate machine learning models for image processing and automated detection of animal infestations, wounds, and other indicators of susceptibility to myiasis; and apply computer vision
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cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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sensing, including Ultrasound and Hyperspectral Imaging (HSI), Artificial Intelligence (AI) and Tiny Machine Learning (TinyML). Duties As a Postdoctoral researcher you are expected to perform both
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity