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Field
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biosafety level 2 (BSL2+) lab), immunology (such as flow cytometry and CAR-T engineering), molecular biology (such as CRISPR, RNA biology), or animal models, (e) collaboration with other researchers both
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high
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genetically diverse mice under a multitude of conditions, and (3) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing
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research career. Research and technical training Training will include hands-on work in the following areas: Developing and validating deep-learning and machine-learning models using echocardiography
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to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers to translate their research
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research Experience with machine learning, deep learning, or multimodal data analysis Experience conducting human-subject research and IRB-compliant studies Experience working with healthcare systems
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these