Optimization of DNN based computer vision algorithms for resource constrained tactical edge

About the Research Objection detection and semantic segmentation-based computer vision algorithms are essential for visual scene understanding on tactical edge computing devices. These algorithms typically rely on complex deep neural network architectures that are computationally intensive and not optimized for resource-constrained edge platforms. In this role, you will profile several computer vision algorithms and optimize them for deployment on edge computing platforms with limited resources. You will also develop a generalized model‑switching framework for dynamic loading of models to balance resource constraints with mission requirements. Responsibilities Profile and benchmark computer vision algorithms on edge hardware. Optimize algorithm performance for resource constraints. Design and implement a model‑switching framework for dynamic model loading. Collaborate with internal and external partners to advance project goals. Qualifications Citizenship: U.S. Citizen only. Degree: Master’s Degree or Doctoral Degree in Engineering or related field. Experience with computer vision, deep learning, and edge computing. Knowledge of algorithm optimization techniques for constrained devices. Strong analytical and problem‑solving skills. #J-18808-Ljbffr ORAU