The Sensors Division at STR focuses on technology development for advanced sensor systems to add national security capabilities, particularly in the areas of airborne/surface-based radar, electronic warfare, underwater acoustics, hyperspectral imaging, and EO/IR sensing. Within these areas, we perform work on emerging concepts & technologies, component & system development and prototyping, and experimental campaigns. The Optical & Novel Sensing (ONS) Group within the Sensors Division pursues physics-driven research across multiple domains to provide warfighter and intelligence mission performance unachievable with traditional sensors. Our main customer focus is on Department of War and Intelligence Community research and development programs, with work spanning concept formulation through validation prototyping. As a Principal Algorithm & Signal Processing Researcher, you will develop and apply state-of-the-art algorithms and techniques for HF communications that include signal detection, channel equalization, and geolocation. You'll engage directly with our customers to understand and address their most critical challenges. You'll lead a team of researchers in creating innovative system capability and collaborate with the engineering team to develop and demonstrate and optimize system capabilities. A successful candidate will have demonstrated leadership skills and substantial expertise in several of the following areas: adaptive signal processing, equalization, High Frequency (HF) radio propagation, ionospheric physics, machine learning, algorithm development, algorithm integration, test & evaluation, model & simulation, systems engineering, or communications systems. They will apply first principles of physics to model and simulate realistic environments to support system performance modeling at HF radio frequencies to develop realistic simulations of complex propagation environments. This position requires skills leading multi-disciplinary teams designing, analyzing, integrating, and demonstrating signal processing algorithms for complex systems.
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Job Type
Full-time
Career Level
Principal