Postdoctoral Fellow - Electrical and Computer Engineering - 531149

Pay Grade/Pay Range: Minimum: $54,000 - Midpoint: $67,500 (Salaried E8)

Department/Organization: 214251 - Electrical and Computer Eng

Normal Work Schedule: Monday - Friday 8:00am to 5:00pm

Job Summary: The Postdoctoral Fellow provides for an internship and continuation of scholarly activity and research after achieving the PhD or other doctoral degree under the direction of a senior faculty member who serves as a mentor for the postdoctoral appointee.

Additional Department Summary: The Electrical and Computer Engineering Department at the University of Alabama seeks a Postdoctoral Fellow in SAR/InSAR Remote Sensing for the NASA-funded NISAR-to-Twin project. The project is developed through the Alabama Transportation Institute (ATI), with ALDOT as the initial end user and planned expansion across the southeastern United States through the Center for Regional and Rural Connected Communities (CR2C2). Related research opportunities include flood mapping, soil moisture estimation, and integration of radar observations with hydrologic and environmental data.

This position combines radar signal processing, field measurements, and digital twin implementation for transportation infrastructure monitoring and decision support. Works with an interdisciplinary faculty team and graduate students.

Initial appointment is for one year, with renewal contingent on research productivity, performance and funding.

Required Department Minimum Qualifications: Ph.D. in electrical or computer engineering, remote sensing, geodesy, geophysics, civil engineering, or a closely related field. Candidates will need to have completed their Ph.D. or have it completed by the start of employment.

Skills and Knowledge: Effective communication, interdisciplinary collaboration, and student mentoring skills. Strong SAR/InSAR signal processing foundations. Ability to diagnose errors and assess coherence, geometry, and uncertainty. Proficiency in Python and Linux; geospatial coordinate systems, raster/vector data, and HDF5; and reproducible scientific workflows.

Preferred Qualifications: NISAR RSLC/GUNW/GCOV processing; L-band interferometry and ionospheric correction; persistent/distributed scatterer methods or phase linking; and InSAR/GNSS integration. Machine learning experience relevant to remote sensing, SAR imaging, or geospatial time series, including uncertainty estimation and physics-informed learning. Experience with data assimilation, GeoAI, digital twins, scalable NASA data processing, or infrastructure applications. Experience processing complex SLC data into interferometric products and deformation time series. Field experience with LiDAR, ground-based radar, or RTK GNSS is useful. Experience with ISCE, MintPy, GMTSAR, GAMMA, or comparable tools, with an understanding of their processing roles. Proven record of peer-reviewed publications or technical reports in in IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, or IEEE Transactions on Signal Processing.

Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment background investigation and information obtained from social media and other internet sources. A prior conviction reported as a result of the background investigation DOES NOT automatically disqualify a candidate from consideration for this position. A candidate with a prior conviction or negative behavioral red flags will receive an individualized review of the prior conviction or negative behavioral red flags before a hiring decision is made.

Advertised: 13 Nov 2026 Central Standard Time
Applications close: 13 Dec 2026 Central Standard Time

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