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projects, acquire and analyze experimental data, supervise and mentor undergraduate students, prepare and submit peer-reviewed journal articles, and present their work at professional conferences. They will
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific
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on track to complete all PhD requirements by the expected start date of October 15, 2026. Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with frameworks such as
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal
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, 2024, or be on track to complete all PhD requirements by the expected start date of October 15, 2026. Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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. ● Contribute to open-source code and reproducible pipelines. Basic Qualifications ● PhD (completed or near completion) in Statistics, Biostatistics, Data Science, Computer Science or a closely related field
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, and accomplishments in the field for candidates with recent PhD (within 1 year of application). Applications and supporting materials should arrive by 23 January 2026, for full consideration. However