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Field
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with biological tissue. Duties of the position will include designing optical spectroscopic imaging technology, using numerical simulation and machine learning tools to extract diagnostically-relevant
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machine learning models, natural language processing (NLP), and ontology-based frameworks to enhance simulation, curriculum development, and personalized learning in health professions education. Develop
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monitoring. Design and implement machine learning models to analyze multimodal data (e.g., student behavior, engagement, and performance) to enhance personalized learning. Develop and evaluate GPT-powered AI
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well as strong quantitative and programming skills. Experience with virtual reality development, machine learning, or advanced data analyses and modeling are highly desirable. Position Status Full Time Posting
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-based or machine-learning/AI-based climate modeling (e.g. hydrometeorological and/or atmospheric processes) are particularly encouraged to apply. Position 3 Working with Dr. Kelly Baker , EEH Associate
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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, machine learning, geographical information sciences, and many other topics to help frame and solve the above problems on a national and global scale. The successful candidate will contribute to cutting-edge
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Substantial programming skills using Python or modern C/C++ Experience with machine learning and deep learning libraries Experience building AI models in platforms such as TensorFlow, Keras, or PyTorch
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decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis. Candidates must
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, computer science, artificial intelligence, or a related quantitative field Experience with mathematical/statistical modeling and/or machine learning/Al methods Strong interest in infectious disease epidemiology