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: 278964401 Position: 2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry Description: The Skinnider Lab at Princeton University aims to recruit a postdoctoral
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: 277393135 Position: 2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry Description: The Skinnider Lab at Princeton University aims to recruit a postdoctoral
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 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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] Subject Areas: Artificial Intelligence, Machine Learning and Autonomy Computational Science and Engineering / Machine Learning Computational Biology / Data Analytics Analytical Chemistry / Current
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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
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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of design, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and
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-Sigler Institute for Integrative Genomics and the Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning
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, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and computational
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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