candidates with a Ph.D. will be considered at the Research Scientist level. The primary focus will be development, implementation, and evaluation of signal-processing algorithms to detect, characterize, and mitigate external interference in weather-radar data. The successful candidate will work with simulated and measured complex I/Q data; develop interference detection, excision, filtering, estimation, and mitigation methods; and quantify effects on radar data quality, including reflectivity, radial velocity, spectrum width, and dual-polarization variables. The work will emphasize rigorous, reproducible evaluation over a range of interference levels, radar operating modes, and weather/ground-clutter conditions. The candidate will also contribute to phased array radar analyses relevant to spectrum-efficient weather-radar operation. Depending on background and project needs, topics may include the following: array signal processing, digital beamforming, adaptive nulling, frequency/waveform agility, spectral-footprint characterization, and trade studies involving update rate, sensitivity, interference susceptibility, and spectrum coexistence. The position will involve close collaboration with OU/ARRC radar engineers and scientists and NOAA/NWS technical partners, with access to radar testbeds, high-performance computing resources, and extensive weather-radar datasets. The successful candidate will work primarily in the Radar Innovations Laboratory and National Weather Center in Norman, Oklahoma, in a highly collaborative environment involving OU faculty, engineers, students, NOAA scientists, and other federal and industry partners. Salary will be competitive based on experience and qualifications, and OU offers competitive benefits. The initial appointment is expected to be one year, renewable for a second year subject to satisfactory performance and continued funding. Qualifications: Requirements: - M.S. or Ph.D. in Electrical Engineering, Electrical and Computer Engineering, or a closely related discipline by the start date. - Strong background in digital and/or statistical signal processing, including areas such as spectral analysis, detection and estimation, adaptive filtering, or time-series analysis. - Programming experience for scientific computing and data analysis, preferably in MATLAB and/or Python. - Ability to work with large complex-valued datasets and communicate technical results clearly in reports, presentations, and collaborative discussions.
