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] Subject Areas: Computational Biology / Data Analytics Analytical Chemistry / Current Advances in Chemistry & Biochemistry Machine Learning / Machine Learning Computational Science and Engineering
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learning (AI/ML) being a major focus. Many of the laboratory's interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 271598471 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting July 2024, and will remain open until excellent fits are found. Successful candidates will
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: 274510667 Position: Postdoctoral Research Associate Description: The Princeton Center for Statistics and Machine Learning (CSML) invites applications for DataX Postdoctoral Research Associate positions
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PI275347872 Create a Job Match for Similar Jobs About Princeton University Princeton University is a vibrant community of scholarship and learning that stands in the nation's service and in the service of all
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Create a Job Match for Similar Jobs About Princeton University Princeton University is a vibrant community of scholarship and learning that stands in the nation's service and in the service of all nations
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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to work on an NIH-funded multi-lab collaborative project studying the neurocomputational basis of reinforcement learning in rodents. The project, in collaboration with the Berke and Frank labs at UCSF