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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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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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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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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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spatial transcriptomic data. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex
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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
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matter (or other “invisible” new physics signals) at the Large Hadron Collider (LHC) compare high-dimensional collision data against detailed simulations whose likelihood cannot be evaluated, only sampled
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concordance and adverse health outcomes using large -scale healthcare datasets. Working collaboratively with clinicians, researchers, patient partners and data specialists to ensure scientific quality and
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-associated virus (AAV)-based gene therapy treatments for inherited retinal disease (IRD). The successful candidate will generate translational research data required to support the preclinical development
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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research