Developing learning methods using electro-optical sensors for novel applications of autonomous systems, with an emphasis on small Unmanned Aerial Systems (sUAS).
Requirements
- Doctoral degree in a field related to computer vision and machine learning (e.g., electrical engineering, computer science, applied mathematics)
- Expertise in computer vision
- Experience with deep learning
- Strong software skills applied to solving Machine Learning and/or Computer Vision problems
- Experience developing on NVIDIA Jetson products, ROS, CAFFE, OpenCV, or participating in machine learning challenges (preferred)
- Experience with uncertainty quantification in support of approaches to V&V for autonomous systems
- Experience with publication(s) in top tier journals and conferences (e.g., CVPR, RSS, ICRA, AIAA, IEEE)
Responsibilities
- Develop neural networks that rely on electro-optical and other sensor modalities for autonomous applications
- Rapid training, tuning, and testing of neural networks
- Object tracking, scene classification, contextual learning, multi-modal learning, stereo vision, pose estimation, and collision avoidance
- Develop enhanced neural network-based object classifiers that provide sub-object descriptions of the resulting classification
- Develop approaches to imbue Verification & Validation (V&V) into mission planning and execution via AI explainability (XAI) in training, decision-making, and object recognition
- Develop methods for uncertainty quantification in object recognition
- Develop methods for analyzable trajectories and natural interaction for human-machine teaming
Other
- 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
- Doctoral Degree
- Research proposal, Three letters of recommendation, Official doctoral transcript documents (required for application)
