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cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a
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(ORNL) is seeking a highly motivated Postdoctoral Researcher with expertise in artificial intelligence and machine learning (AI/ML), remote sensing, Earth and environmental sciences, and the analysis
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of machine learning methods for archaeological predictive modelling / site discovery Contribute to the design of the archaeological search-process simulator used to train and evaluate AI methods Write grant
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technologies. The candidate will work at the intersection of Artificial Intelligence, Scientific Machine Learning, and Energy Materials, contributing to the design of self-driving laboratories and no-code
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-truth data collection, and operating, testing, validating, and refining crop models to improve prediction accuracy. The candidate will also contribute to the development and application of machine
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. Candidates with background knowledge and hands-on experience in mouse models, proteomics, 3Dorganoids,primary cells purification and culture skills are particularly welcome. Minimum Requirements: Ph.D
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Job Type Staff Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived
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system integration A record of peer-reviewed publications Experience with AI/ML-based control, such as reinforcement learning, learned or model-predictive control, or vision-guided manipulation Experience
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 22 days ago
include advancing foliar trait retrieval algorithms, atmospheric correction over dense humid tropical canopies, machine learning and dimensionality reduction for handling big data, landscape evolution