152 parallel-computing-numerical-methods Fellowship positions at Harvard University in postdoctoral
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: Computational methods development and consortium data management for the Human Virome Program, with the mandate to characterize viral (phage and eukaryotic) communities across the human body in health and disease
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these methods to address challenges in scientific discovery and precision medicine. We seek highly motivated applicants with a background in one or more of the following areas: agentic AI, geometric deep learning
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larger collaborative research program. The postdoctoral fellow will be part of a multidisciplinary team at Harvard and will collaborate with research groups outside Harvard involved in the broader project
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these methods to address challenges in scientific discovery and precision medicine. We seek highly-motivated applicants with background in one or more of the following areas: agentic AI, geometric deep learning
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these or related fields, particularly those who may bring a new technology, method, or perspective to bear on the work of the Center. We are especially interested in candidates with strong quantitative, data
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Fellowship program supports postdoctoral researchers at the Museum of Comparative Zoology (MCZ) at Harvard University to pursue the discovery and formal taxonomic description of Earth’s animal species. Fellows
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and laboratory space in Boston’s Fenway District. Our space has multiple amenities which include: roof top terrace, fitness center, locker room, bike storage, and close proximity to numerous restaurants
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Templeton Foundation (Grant #63556), seeks to recruit one Postdoctoral Research Fellow for Ethnos-MH, a novel, global R/S competency program based on ethnographic methods for mental health professionals
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internal and external meetings Contribute to recommendations on next steps for experiments while also taking an active role overall in program strategy and alignment with the sponsor’s contractual
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/. The postdoctoral research fellow will work on causal inference projects under the supervision of Professor Miguel Hernan. The projects range from the development of causal inference and AI methods