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will work with Prof. Daniel Eisenstein and collaborators on the analysis and interpretation of JWST data, with particular emphasis on deep-field observations. The position provides access to large, high
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. Experience with cell state modeling from large-scale perturbation datasets, including Perturb-seq or related data. Experience with multimodal modeling for protein function or cellular state prediction
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large-scale biological datasets. You will be directly mentored by a postdoctoral fellow, Dr. Josh Tycko, providing exposure and advice for the research career track. You will benefit from the rich
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, tool use, retrieval systems, or related AI systems Experience with large-scale datasets, distributed training, or high-performance computing environments Interest in scientific applications of AI/ML
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outstanding research in computational physics for modeling of complex physical phenomena. Additional Qualifications Candidates must have experience in carrying out large-scale computational modeling of complex
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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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the intersection between quantum information and particle physics. The successful applicant will work in Professor Carlos Argüelles’ group on the development of new algorithms to encode and process particle physics
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Qualifications Applicants should submit a cover letter, CV, list of publications, and a research proposal explaining research interests (no page limit). Please also provide contact information for three references
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should submit a cover letter, CV, a brief research proposal, and two to three references’ names and contact information. Applications will be reviewed on a rolling basis until the position is filled
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Special Instructions Interested candidates should submit a cover letter, CV, a brief research proposal, and two to three references’ names and contact information. Applications will be reviewed on a rolling