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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 19 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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Universe (KMI)-PD [#31232, KMI-2025-2] Position Title: Position Type: Postdoctoral Position Location: Nagoya, Aichi 464-8602, Japan [map ] Subject Area: AI/Machine Learning / Astronomy Appl Deadline: 2025
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning
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allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high
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work closely with I-CREWS researchers across the state. The position emphasizes developing, integrating, and applying modeling approaches—including machine learning (ML), hydrologic and energy systems
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to develop synthetic cells that mimic tubular function by integrating transport proteins in lipid membranes. Furthermore, the ultimate long-term goal is to integrate these cells in a dialysis machines. The
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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healthcare and foundational biological science. The primary focus will be on designing and implementing multimodal, agentic AI models for clinical support and creating novel deep learning models for genomics
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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partnership between academia and industry to drive research and development forward. Project description This project aims to develop unsupervised machine learning methods for extracting dynamical models