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are particularly relevant. The developed numerical models will leverage a large suite of in-situ and remotely sensed observational datasets, spanning from subsurface to atmosphere. You will have experience
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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based on fossils. This will make it possible to pinpoint cognitive changes in geological time, which can be correlated with major events in the history of Earth. More information on our team can be found
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public health. In this role, you will develop and evaluate novel AI and machine learning methods using large-scale multimodal datasets, contributing to epidemiology-informed foundation models, predictive
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to manage, organize, and back up large amounts of image and tabular data generated by program and collaborator labs. Other Requirements: -May have to work on the weekend and/or after normal hours. -Travel is
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extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC
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Large Earthquakes”, led by a team of researchers at Durham and Oxford University in collaboration with international partners. The project builds on a multidisciplinary approach combining laboratory
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skills and strong publication records to apply. Some skills in analyzing/evaluating large data sets are highly desirable. Knowledge of an advanced programming language such as MATLAB, Python, etc. is
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cultural activities; and more! The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocations services
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and