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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 11 minutes ago
. Description: This project aims to develop a next-generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling
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using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Successful
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Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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for candidates with a strong computer science background, such as algorithms, machine learning and data science. Key Responsibilities Develop, implement, and evaluate machine learning and deep learning models
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in Ithaca, NY with a focus on developing deep learning algorithms. Dr. Haiyuan Yu, Ph.D. is a Tisch University Professor of Computational Biology in the College of Agriculture and Life Sciences and a
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doctoral work focused on deep learning methods and who have a strong interest in genomics will also be considered. Experience: At least one publication in computational genomics or machine learning methods
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Cultural Studies, History, or related field Demonstrated expertise with large language models (fine-tuning, prompting, deployment) Strong Python programming with deep learning frameworks (PyTorch, TensorFlow
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 12 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal