Principal Research Scientist: Signal Processing

RTX•Arlington, VA

3d•$118,300 - $224,900•Onsite

About The Position

RTX BBN solves challenging problems of high importance to our customers in the Department of Defense, the Intelligence Community and Industry by rapidly prototyping systems for sensing complex physical phenomena, correlating them to events of interest, and producing high-value, actionable information from the sensed data. The team specializes in delivering networked sensor system solutions by combining a strong understanding of the underlying physics, high-fidelity modeling, simulation of complex signatures and propagation phenomenology, deep expertise in signal processing, EM and RF phenomenology, and computer science, proven capabilities in system development, and mature tools to enable rapid design cycles from theory to demonstration. We are currently searching for a highly qualified Signal Processing Research Scientist to join our team in Arlington, VA.

Requirements

M.S. in Electrical Engineering, Mathematics, or Physics plus 5+ years of relevant experience, or Ph.D. plus 3+ years experience.
Experience with signal processing, including estimation, detection, etc.
Experience with RF modeling and sensing.
Active Top Secret government security clearance

Nice To Haves

Position may require some overnight travel.
Experience applying advanced methods, theories, and R&D techniques to solve difficult problems.
Skillset to tackle research challenges proactively with minimal supervision and periodic check-ins with team leads.
Experience leading small teams of technical contributors.
Experience documenting plans and scientific findings with opportunities to present to government customers.

Responsibilities

Statistical signal processing: Estimation techniques including Bayesian and non-Bayesian estimators, performance measures such as bias and mean-square error and bounds on estimator performance such as the Cramer-Rao lower bound. Detection techniques including the Neyman-Pearson detection, probabilities of detection and false alarm, receiver operating curves, etc.
Cyclostationary signal processing: CAF, SCF, SOF estimation. Computation of Cyclic Cumulants. Cyclic Feature Detection.
Adaptive signal processing: Temporal and space-time adaptive processing. Interference covariance estimation techniques including both structured and reduced-rank techniques. Wiener filtering and on-line adaptive techniques such as LMS and RLS. Kalman filtering, particle filtering, and multi-hypothesis tracking. Machine learning and optimization for optimal control.
RF propagation and signature modeling: Link budgets, terrestrial and free-space propagation models, multipath, non-stationary propagation environments (Doppler spread), clock stability and phase noise, and target kinematics. Antenna pattern estimation. Estimation of bistatic RCS.
Sensing: Sparse array processing, beamforming, direction finding, filtering and tracking. Range-Doppler radar and synthetic aperture radar (SAR). Signal classification and recognition.
Physical-layer communications: Modulation techniques and tradeoffs, channel estimation and tracking, MIMO communications, equalization, forward error correction and decoding. LPX waveform design. Multi-user detection.