12 deep-learning-phd "Computer Vision Center" Postdoctoral research jobs at Harvard University
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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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-relevant research related to aspects of the Medicare program, including payment policy, risk adjustment, and competition. Experience working with Medicare claims data, a deep understanding of econometrics
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-relevant research related to aspects of the Medicare program, including payment policy, risk adjustment, and competition. Experience working with Medicare claims data, a deep understanding of econometrics
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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
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astrophysics, exotic core-collapse supernovae, and machine learning methods for time series analysis. A PhD in Physics, Astronomy, or a closely related field is required. The position will entail work on a
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by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW
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Description Join our dynamic research team at Harvard University and spearhead groundbreaking research at the intersection of generative AI, multimodal learning, and Earth sciences. We are seeking a highly
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Description Join our dynamic research team at Harvard University and spearhead groundbreaking research at the intersection of generative AI, multimodal learning, and Earth sciences. We are seeking a highly
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for opportunities abroad. These grants present an excellent opportunity for recently minted scholars to deepen their expertise, to acquire new skills, to work with additional resources, and to make connections with
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly