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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 10 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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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. The Stanford Solutions
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About the Opportunity Job Summary The Data-Driven Renewables Research (D2R2) group led by Dr. Peter Schindler is accepting applications for a postdoctoral research associate in the field
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fellowships and NSF SBE postdoctoral awards. We especially welcome applicants with theoretical interest in child language development, strong computational and analytical skills (deep learning frameworks), and
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/20 05:00 AM) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological Systems Duke University – Departments of Mathematics and
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electrophysiology. We are seeking a postdoctoral researcher to lead the neuroimaging component of a longitudinal, pediatric drug trial in Neurofibromatosis type 1 (NF1). In this role, you will acquire and analyze
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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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