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Machine Vision and Learning for eXplainable AI (XAI) Join to apply for the Machine Vision and Learning for eXplainable AI (XAI) role at ORAU Machine Vision and Learning for eXplainable AI (XAI) 2 months ago Be among the first 25 applicants Join to apply for the Machine Vision and Learning for eXplainable AI (XAI) role at ORAU Get AI-powered advice on this job and more exclusive features. Organization National Aeronautics and Space Administration (NASA) Organization National Aeronautics and Space Administration (NASA) Reference Code 0017-NPP-NOV25-LRC-Aero How To Apply All applications must be submitted in Zintellect Please visit the NASA Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal Three letters of recommendation Official doctoral transcript documents Application Deadline 11/1/2025 6:00:59 PM Eastern Time Zone Description About the NASA Postdoctoral Program The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly-talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA-affiliated research institute. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology. Description: NASA Langley Research Center is seeking a postdoctoral researcher with expertise in computer vision to develop learning methods (including deep learning) using electro-optical sensors to explore novel applications of autonomous systems, with an emphasis on small Unmanned Aerial Systems (sUAS). The successful candidate should hold a doctoral degree in a field related to computer vision and machine learning (e.g., electrical engineering, computer science, applied mathematics) and will perform cutting-edge research as a team member in the project ATTRACTOR: Autonomy Teaming & TRAnsparency for Complex Trusted Operational Reliability. The objective of ATTRACTOR is to develop approaches to imbue Verification & Validation (V&V) into mission planning and execution via AI explainability (XAI) in training, decision-making, and object recognition; analyzable trajectories; natural interaction for human-machine teaming; and persistent modeling and simulation for engendering justifiable trust in autonomous systems. This requires enhanced neural network-based object classifiers that provide sub-object descriptions of the resulting classification in an effort to develop explainability. This position will involve the development of neural networks that rely on electro-optical and other sensor modalities for autonomous applications within the ATTRACTOR project goals, especially V&V and uncertainty quantification in object recognition, and should lead to publication(s) in top tier journals and conferences (e.g., CVPR, RSS, ICRA, AIAA, IEEE). Possible candidate tasks include: rapid training, tuning, and testing of neural networks, object tracking, scene classification, contextual learning, multi-modal learning, stereo vision, pose estimation, and collision avoidance. All developed methods and products must be accompanied by uncertainty quantification in support of approaches to V&V for autonomous systems. The candidate should include a list of publications related to deep learning (and preferably pre-prints for articles that may be under review). Demonstrated strong software skills applied to solving Machine Learning and/or Computer Vision problems is a pre-requisite. Experience developing on NVIDIA Jetson products, ROS, CAFFE, OpenCV, or participating in machine learning challenges are pluses. Location: Langley Research Center Hampton, Virginia Field of Science: Aeronautics Advisors: Bonnie Allen 757-864-7364 Eligibility is currently open to: U.S. Citizens; U.S. Lawful Permanent Residents (LPR); Foreign Nationals eligible for an Exchange Visitor J-1 visa status; and, Applicants for LPR, asylees, or refugees in the U.S. at the time of application with 1) a valid EAD card and 2) I-485 or I-589 forms in pending status Questions about this opportunity? Please email Point of Contact Mikeala Eligibility Requirements Degree: Doctoral Degree. Seniority level Seniority level Internship Employment type Employment type Full-time Job function Job function Engineering and Information Technology Industries Government Administration Referrals increase your chances of interviewing at ORAU by 2x Get notified about new Machine Learning Specialist jobs in Hampton, VA . 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