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, computer science, or a related field; experience in machine learning and developing and validating computational analysis workflows; attention to detail; excellent interpersonal, analytical, problem-solving
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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machine learning, deep learning, data visualization, and applied analytics for multi-modal datasets. Technical proficiency with Python, R, SQL, SPSS, Tableau. Architectural and design software expertise
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the results, and communicates with the group members. Writes computer codes for the above data modalities under the guidance of the team leader. Engages in the development and testing/validation of new
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based methods. As part of a
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 9 hours ago
on the principle that by integrating high-resolution Earth observation (EO) data from NASA with state-of-the-art machine learning, we can produce a more accurate, dynamic, and actionable measure of wildfire risk
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image reconstruction methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based
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on the analysis of different characteristics of the skin and body fluids, such as sweat and blood. Many of these methods are challenged with quality (accuracy/precision of measurement), power consumption, usability