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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 11 hours ago
Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S. Lawful
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options Employee and dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits
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from noisy, high-dimensional data. Strong programming skills (e.g., Python, R, C++) for handling large datasets and implementing models. A genuine curiosity about how science works and a desire to ask
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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Polarization Lab for scientific research on human-AI interaction on social media. The ideal candidate will possess advanced knowledge of R and/or Python software, have experience performing statistical analyses
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) • Big data pipelines, distributed computing, and geospatial data processing • Python, R, SQL/NoSQL, containerization (Docker), Kubernetes • API development and web-based analytics tools • Systems
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2021; Khoshkhoo et al. JAMA Neurology 2023; Farrell et al. Nature 2024; Bizzotto et al. PNAS 2025; Khoshkhoo et al. bioRxiv 2026. For more information, please check our websites at https
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. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
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advanced signal processing and time–frequency analyses of neural recordings. Develop and implement computational pipelines using MATLAB, Python, or similar platforms. Lead manuscript preparation for peer
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to see the following publications for some context: https://pubs.rsc.org/en/content/articlehtml/2024/gc/d4gc00765d https://pubs.rsc.org/en/content/articlehtml/2024/gc/d4gc04533e https