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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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, 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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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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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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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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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
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biology and genomics. We are collaborative and multidisciplinary, combining organic chemistry, cell and molecular biology, protein biochemistry, and large-scale genetic screening in our research approach
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large multi-modal datasets and who wish to deploy the next generation of exposome AI models are highly encouraged to apply. This position comes with data ready to analyze, and you can focus on developing