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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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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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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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modern machine learning and a strong record of research accomplishment who are excited to build brain foundation models and other AI systems that advance our understanding of neural activity, brain
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will lead and participate in observations and data analysis across the electromagnetic spectrum, or will lead work on machine learning classification of optical transients. Applicants with previous
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, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in
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Smithsonian Astrophysical Observatory (SAO). We welcome applicants, who as a component of their research statement, discuss their experience in mentoring, outreach, and teaching. Current senior members
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data in near-term era quantum computers. Applicants with backgrounds in quantum information or particle physics are both encouraged to apply. Candidates with strong expertise in machine learning, quantum
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and at national/international conferences. Collaborate with an interdisciplinary team of biostatisticians, computer scientists, and climate scientists. Contribute to open-source code, reproducible
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: Sitting using near vision use for reading and computer use for extended periods of time. Lifting (approximately 20 to 30 pounds), bending, and other physical exertion. As part of your application, we