Research Engineer, C++, GNSS Signal Processing
Join our innovative research team dedicated to advancing Global Navigation Satellite System (GNSS) technologies.
As a Research Engineer specialising in GNSS signal processing, you'll play a pivotal role in our research efforts to improve GNSS positioning performance.
You'll work on cutting-edge projects, developing novel algorithms and techniques to mitigate errors, enhance signal integrity, and optimise positioning accuracy in challenging environments.
Key Responsibilities:
Conducting research to advance GNSS signal processing techniques. Designing and implementing algorithms for error correction, multipath mitigation, and signal enhancement.
Experience: A strong background in signal processing, particularly in the context of GNSS or related technologies. Experience with GNSS receivers, signal processing algorithms, and familiarity with tools such as MATLAB or Python for algorithm development and simulation is essential. Knowledge of error sources in GNSS, such as ionospheric and tropospheric effects, multipath, and receiver noise, is highly desirable. Experience with Kalman filters and other probabilistic estimation techniques.
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Join our innovative research team dedicated to advancing Global Navigation Satellite System (GNSS) technologies.
As a Research Engineer specialising in GNSS signal processing, you'll play a pivotal role in our research efforts to improve GNSS positioning performance.
You'll work on cutting-edge projects, developing novel algorithms and techniques to mitigate errors, enhance signal integrity, and optimise positioning accuracy in challenging environments.
Key Responsibilities:
Conducting research to advance GNSS signal processing techniques. Designing and implementing algorithms for error correction, multipath mitigation, and signal enhancement.
Experience: A strong background in signal processing, particularly in the context of GNSS or related technologies. Experience with GNSS receivers, signal processing algorithms, and familiarity with tools such as MATLAB or Python for algorithm development and simulation is essential. Knowledge of error sources in GNSS, such as ionospheric and tropospheric effects, multipath, and receiver noise, is highly desirable. Experience with Kalman filters and other probabilistic estimation techniques.
#J-18808-Ljbffr
