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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus
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English language skills. Excellent collaboration and communication skills. Meriting factors for the position Experience with other types of computational methods, such as population simulations, machine learning
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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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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; data assimilation; scientific machine learning and AI applied to wildfire prediction; smoke and fire-atmosphere interactions; and experimental or observational approaches supporting wildfire model
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package, including health and life insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https
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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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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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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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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