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Details Title Fellow: Reinforcement Learning for Large Scale Foundation Model Post-Training School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer
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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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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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and/or slitless spectroscopy datasets and/or JWST imaging datasets Development of methods for large spectroscopic surveys and data products This is a two-year term appointment from date of hire with
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these conditions are shaped by genetic factors using large-scale data and sample collection. The successful candidate will ensure the project effectively builds its digital cohort and manages the related biospecimen
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working on public health, health services, and/or epidemiological research projects strongly preferred Strong quantitative background, experience working with large data sets and statistical and data
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Machine Learning Engineer with advanced expertise to lead development of large language models (LLMs) to advance CCB’s mission to leverage data and computation to transform research and education, and to
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and analyze large multimodal datasets, with data ready to analyze you can focus on developing research findings resulting in publication.Responsibilities include:Research and translate exposome data
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Strong quantitative background, experience working with large data sets and statistical and data management Desire to learn new modelling approaches and statistical methods as necessary for the successful
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voting information. The project integrates large-scale behavioral web browsing data and survey data to map digital information pathways and assess their association with civic participation outcomes. Job