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, Enviromental, 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.

The new EU Horizon project advances safe active mobility uptake and research by introducing a human-centred, evidence-based approach that integrates actual and perceived safety for pedestrians, cyclists, and micromobility users. Moving beyond conventional crash-focused approaches, it captures near-misses, dynamic interactions, and embodied safety experiences that shape behaviour and mode choice. The project combines multi-source traffic, infrastructure, vehicle, and health data with immersive eXtended Reality (XR) experimentation and explainable Artificial Intelligence to analyse safety-critical situations that are rare, underreported, or ethically impossible to observe in real traffic. Explainable AI ensures transparency and interpretability, supporting trust, transferability, and policy relevance. The project translates these insights into harmonised assessment methodologies, predictive models, and validated indicators, enabling robust evaluation and comparison of regulatory, infrastructural, technological, and behavioural interventions across Safe System Approach stakeholders. Special focus is placed on interactions between users with differing masses and speeds, including e-bikes, e-cargo bikes, and e-scooters, for both personal mobility and urban logistics. Large-scale pilots in four European cities validate methods in real traffic, support cross-city learning, and ensure applicability under diverse safety, infrastructure and cultural conditions. Implemented by a multidisciplinary consortium bridging engineering, behavioural science, XR, AI, urban planning, and policy, the project delivers actionable, standardised guidance that accelerates safer, more inclusive active and micromobility systems across Europe.

Planning safe urban transport systems is inherently complex: interventions last for decades, require significant investment, must fit within constrained spaces, and must satisfy ever-changing user needs. Modern data collection methods - such as high-frequency on-board vehicle sensors and computer-vision imagery - are well-suited to capture these complexities via near real-time, high-resolution insights. However, collecting, processing, and operationalizing these big data volumes is a challenge due to heterogeneous data structures and heavy computational demands.

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

• 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

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.

We look forward to receiving your online application before 31 July 2026 including the following documents:

• Letter of interest including your ideas of potential research in the project
• A curriculum vitae (with list of publications, if applicable, and contact information of at least two referees)
• Grades of all university courses taken as well as diplomas

Further information about the Institute of Construction and Infrastructure Management can be found on our Website. Questions regarding the position should be directed to Ms. Nathalie Dietrich, [email protected] (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Screening of applications starts on 1 August 2026. Applications will be accepted until the position is filled.

The preferred start date is 1 November 2026, although others are possible.