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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0147-NPP-NOV25-ARC-EarthSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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Organization U.S. Department of Defense (DOD) Reference Code USAISR-2025-0004 How to Apply Click on Apply at the bottom of the opportunity to start your application. Description The U.S. Army
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cytoskeleton and membrane trafficking. • Utilize quantitative, biophysical, and computational approaches to cell biology, including fluorescence imaging, image analysis, and biophysical modeling. • Experiment
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in AI-modeling, Monte Carlo simulation, and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize
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Position Description The Kim Lab is seeking a full-time postdoctoral scholar with experience in single-unit/LFP recording and/or calcium imaging in rodents. This NIMH-funded position focuses on investigating
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(33%) • Conduct full stack development efforts including UI/UX, code maintenance, and deployment pipelines (33%) • Evaluate and refine AI models using annotated data and real-world image datasets (10
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a motivated Doctoral Researcher with strong coding/signal analysis background to join
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disease with a focus on Alzheimer’s Disease. Computational models will be developed that utilize data obtained from a wide range of experiments, from basic biochemical methods to advanced imaging techniques
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems