Job Summary

Satellite positioning (GNSS) underpins smart-mobility services such as road usage charging, but in dense cities it is vulnerable to challenging scenarios such as urban canyons, dense foliage, tunnels, carparks, and stacked or closely spaced roads, as well as to signal interference such as jamming or spoofing.  In this role you will develop data-driven decision-support tools for the planning, validation, and management of vehicle positioning based services, with clear confidence measures for reliable decisions. You will develop AI and signal-processing methods to achieve robust and accurate vehicle positioning by leveraging multi-sensor fusion of GNSS, INS, V2X, and related data sources to improve positioning in GNSS challenged environments. You will apply your research expertise to develop innovative solutions for high-impact operational challenges.

Required Profile

• PhD in Electrical/Computer/Systems Engineering, Computer Science, AI, Operations Research, Applied Mathematics or a related field.
• Expertise in data analytics and signal processing algorithms, with demonstrated ability to apply machine learning and AI techniques to real world problems.
• Good to have experience in efficient management of spatio-temporal data: spatial and time-series databases, spatial indexing, partitioning and query optimization, and batch processing frameworks.
• Strong software development skills in C++ and Python. Hands-on experience with system design, modular architecture, and interfacing between multiple components.
• Familiarity with AI-assisted development workflows, including the use of AI tools for software development lifecycle (SDLC) automation to accelerate solution development.
• Ability to engage stakeholders to formulate use cases and system requirements. Experience in designing system architecture for integrated software solutions to deliver research outcomes.
• Strong problem-solving skills, and the ability to work independently and collaborate effectively with team members.
• Good to have background in intelligent transportation systems or smart mobility.

What You Will Do

• Develop algorithms and software prototypes. Design test cases and conduct field trials to validate the algorithm performance.
• Analyse large-scale vehicle trajectory and positioning data and develop approaches for spatio-temporal analysis of raw GNSS data to characterize signal quality and positioning performance across the road network. Identify road segments with consistently high positioning quality, perform anomaly detection, and root cause analysis for low accuracy segments. Utilize uncertainty aware methods for decision-support systems.
• Develop INS-based dead-reckoning algorithms using Kalman-filter variants. Build AI-based multimodal fusion and tracking methods for real-time positioning using GNSS, INS, V2X, and other relevant data sources; develop map-feature extraction and road-segment matching methods for GNSS-denied conditions.
• Develop algorithms that detect jamming, spoofing and blocking of GNSS signals using hierarchical fusion of multi-sensor data; build tracking algorithm for reliable detection of GNSS interference and flag suspicious behaviour.