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the Department of Chemistry to develop innovative strategies for generating Machine Learning Interatomic Potentials (MLIPs) that accurately capture the dynamic nature of metal-ligand interactions. These models
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Summary We are seeking an undergraduate student to assist with data preprocessing and machine learning tasks. Career Readiness Competencies: Communication Professionalism Teamwork Essential
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the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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implement computer vision pipelines for crop monitoring, plant stress detection, and disease identification in greenhouse environments. Apply machine learning and deep learning models (semantic segmentation
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University of California, San Francisco | San Francisco, California | United States | about 10 hours ago
language models, machine learning, or AI-driven data analysis Strong programming skills (Python required; R and/or Julia a plus) Experience with large language models, machine learning, or AI-driven data
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-France 75 005, France [map ] Subject Areas: Machine Learning Statistical Physics Appl Deadline: 2026/01/15 11:59PM (posted 2025/11/04, listed until 2026/05/04) Position Description: Apply Position
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 44 minutes ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed
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or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model