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Details Title Pre-Doctoral Fellow: Reinforcement Learning for Large Scale Foundation Model Post-Training School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area
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Code, Codex, or similar systems, in research and development workflows Experience in computational neurobiology, neural data analysis, or modeling neural activity from large-scale recordings Ability
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development workflows Experience in computational neurobiology, neural data analysis, or modeling neural activity from large-scale recordings Ability to work effectively in a collaborative research environment
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structure modeling, protein–protein docking, or small-molecule–protein docking Experience with cell state modeling from large-scale perturbation datasets, including Perturb-seq or related data Experience with
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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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large-scale datasets, distributed training, or high-performance computing environments Interest in scientific applications of AI/ML, including the life sciences Interest in alternative architectures and
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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