Strong proficiency in state estimation techniques, including Kalman filtering (EKF, UKF) and batch filtering.
Expertise in non-linear optimization and computational mathematics.
Proficient in Python AND C/C++.
Experience working with GNSS, radar, lidar, and camera data, including sensor calibration and error modeling in real-world applications/projects.
Experience with modern software engineering practices: version control (Git), CI/CD, cloud-based workflows, and peer review.
4+ years of industry experience with a Master’s or PhD in Aerospace Engineering, Electrical Engineering, Mechatronics, or a related field.
Responsibilities:
Design and implement probabilistic filtering techniques (e.g., Kalman, Extended Kalman, Particle Filters) for robust real-time state estimation and uncertainty modeling.
Develop sensor fusion algorithms that integrate data from multiple modalities, such as GNSS, LiDAR, radar, and cameras, to estimate real-time state estimation and tracking.
Develop algorithms for outlier rejection, fault detection, and state smoothing.
Contribute to architecture development, concept-of-operations, and technology trade studies.
Collaborate with AI/ML engineers to explore machine learning-enhanced state estimation techniques