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domain-specific foundation models that support tasks such as forecasting, interpolation/extrapolation, downscaling, and “what-if” scenario analysis relevant to climate-related health risks and adaptation
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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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related field: Computer Science Statistics / Biostatistics Applied Mathematics Data Science ● Demonstrated expertise in modern machine learning, including at least one of the following: Spatiotemporal
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, full-time, at Harvard University. Remote work for this position is not possible. Basic Qualifications PhD in computer science, statistics, electrical engineering, applied mathematics, computational
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computational biology or biological data analysis. Strong candidates may come from protein-focused, cell-state-focused, or multimodal biological modeling backgrounds and will have expertise in one or more of the
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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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. Specifically, our core technique is scanning electrochemical cell microscopy (SECCM) , a powerful electrochemical imaging method for probing electrochemical processes at nanoscale sites on complex electrodes. We
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Qualifications: Doctoral degree. Applicants background may include studies in: Computational Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications: Expertise (or desire
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Models Basic Qualifications: A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong
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information, and/ or data analysis are strongly encouraged to apply, as these skills will be preferred in the selection process. This appointment duration is up to three years, with reappointment contingent