PhD Position in Vehicle Sensor and Remote Sensing Analysis for Road Safety

This doctorate aims to advance the state-of-the-art in sensor and remote sensing data fusion for urban transport infrastructure safety analysis. The candidate will develop new tools and methods that integrate big data, computer vision, and machine learning. The candidate’s core tasks will include: Develop a Scalable Sensing Pipeline: Design and implement a software/tool architecture capable of processing multi-source vehicle sensor, camera, and remote sensing data streams; Automate Feature & Factor Identification: Train machine learning and computer vision models within the pipeline to automatically detect built-environment infrastructure characteristics and related factors affecting micromobility safety and comfort; Generate Mapping & Diagnostic Outputs: Ensure the software accepts diverse inputs and generates structured, high-quality data outputs optimized for spatial mapping, risk diagnosis, and downstream predictive safety modelling; Collaborate via Real-World Pilots: Validate and refine the pipeline using real-world data from the project’s pilot cities, working in close cooperation with international consortium partners.

The Chair of Infrastructure Management, led by Professor Dr. Bryan T. Adey within the Institute of Construction and Infrastructure Management of the Department of Civil, Environmental, and Geomatic Engineering, has an opening for a PhD student. This position focuses on leveraging vehicle sensors, remote sensing, and machine learning to support modern urban road safety analysis as a part of a larger EU project. ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

• A Master’s degree in urban analytics, artificial intelligence, computer science, transport planning/engineering, geomatics, or a related field

• A good grasp of machine learning, computer vision techniques, statistics, and signal processing

• High proficiency in programming environments (e.g., R, Python) and spatial analysis tools (GIS)

• Good knowledge of English (professional proficiency, written and spoken)

• Knowledge of German is beneficial