SARlab recently won an NSERC Alliance grant with industrial partner aiRadar and goverment partner BC Wildfires titled "Researching airborne and drone based mmWave and microwave synthetic aperture radar capabilities for wildfire risk, and severity mapping, plus active fire-fighting support".
The project aims to research next-generation airborne and drone-based Synthetic Aperture Radar (SAR) systems to improve wildfire risk mapping, post-fire assessment, and real-time firefighting support under smoke, darkness, and poor visibility. By combining adaptive radar technologies with software-defined sensing, the research aims to deliver agile, deployable imaging systems for more effective wildfire monitoring and response.
PhD Position 1: mmWave Radar & SAR Systems for UAV and Airborne Remote Sensing
We are seeking a highly motivated PhD candidate to develop a next-generation mmWave radar emulation platform for advanced ground- and UAV-based imaging radar systems. The project aims to bridge numerical simulation and physical testing by creating a flexible testbed for validating novel synthetic aperture radar (SAR) concepts.
The research will focus on topics including:
- Signal-based navigation and motion compensation for high-resolution SAR imaging
- Single-pass interferometric SAR (InSAR) and topographic mapping
- Hybrid/digital beamforming and cognitive SAR
- Resilient Ka-band SAR technologies
- Bi-/multistatic radar for environmental monitoring
- Efficient raw radar data compression and onboard processing
The candidate will work with laboratory and UAV-acquired mmWave SAR data, as well as airborne microwave SAR datasets, and participate in field campaigns in the Yukon. Applications include wildfire monitoring, forest biomass and vegetation health assessment, snow and soil mapping, and subsurface sensing.
Qualifications: Applicants should have a strong background in electrical engineering, physics, computer engineering, or a related field, with interests in radar signal processing, remote sensing, wireless systems, machine learning, or autonomous sensing.
This topic offers the opportunity to contribute to a unique radar research capability with strong national and international collaborations and broad environmental and geoscience applications.
PhD Position 2: Real-Time UAV & Backpack Imaging Radar for Wildfire Response
We are seeking a PhD candidate to develop real-time imaging radar systems for UAV and backpack platforms to support tactical wildfire operations. The project addresses the challenge of producing high-quality SAR images from highly unstable moving platforms operating in smoke and adverse weather.
Research topics include:
- Multi-aperture radar micro-navigation and motion compensation
- Real-time SAR autofocus and image formation
- Data-driven trajectory estimation using radar and optical Structure-from-Motion (SfM)
- Polarimetric SAR imaging and coherence change detection for wildfire monitoring
The candidate will work with airborne, UAV, and laboratory mmWave radar systems to develop prototype backpack and UAV SAR sensors capable of robust real-time imaging.
Qualifications: Applicants should have a strong background in electrical engineering, computer engineering, physics, or a related discipline, with interests in radar signal processing, robotics, autonomous systems, computer vision, or remote sensing.
This topic offers opportunities to develop cutting-edge radar technologies with direct applications in emergency response and environmental monitoring.
PhD Position 3: AI-Enabled Radar鈥揙ptical Data Fusion for Wildfire Monitoring
We invite applications for a PhD project focused on developing high-dimensional radar鈥搊ptical data cubes for comprehensive wildfire situational awareness before, during, and after fire events. The research combines advanced SAR processing, sensor calibration, and machine learning to create analysis-ready datasets for operational decision support.
Research topics include:
- Calibration and fusion of airborne SAR and optical imagery
- Development of standardized radar鈥搊ptical data cubes
- Machine learning for environmental change detection and anomaly identification
- Analysis-ready geospatial products for wildfire risk assessment and damage monitoring
The candidate will collaborate with wildfire management agencies and research partners to develop prototype workflows using airborne radar and optical datasets collected during field campaigns.
Qualifications: Applicants should have a strong background in remote sensing, electrical engineering, computer science, geomatics, or a related field, with interests in machine learning, geospatial data analysis, radar systems, or environmental monitoring.
This topic provides the opportunity to develop next-generation remote sensing technologies with significant impact on wildfire management and climate resilience.
Please refer to the MSc and PhD positions for general requirements.