PhD Position in Data and AI for Multi-Source Micromobility Safety Analytics

Do you want to uncover how and why micromobility risks emerge by turning complex, messy real world data into AI models that help cities design safer streets?

Job description

You will be working in the SAFE MOVE project on advanced data integration and AI driven safety analytics for micromobility. The data and models developed in the project enable researchers and cities to understand where and why micromobility risks emerge, how infrastructure and behaviour interact, and which interventions can meaningfully improve safety. By producing reliable, interpretable evidence on real time and predicted risks, the position directly supports better informed policy decisions, safer street design, and more effective traffic and mobility management strategies across European urban areas.

In this role, you will work with diverse data sources—ranging from traditional crash and exposure records to shared micromobility traces, onboard sensors, traffic cameras, and citizen reports—that form the foundation for risk modelling. The core of the research lies in developing and advancing explainable AI models that can represent how micromobility risks emerge, evolve, and interact with infrastructure and behaviour. You will investigate modelling questions such as how to fuse heterogeneous data streams into a coherent risk representation, how to capture interaction driven hazards, and how to quantify uncertainty in predictions so that model outputs remain reliable in real world conditions.

Building on the integrated datasets, you will design algorithms capable of detecting safety risks, identifying unsafe behaviours, and predicting emerging hazards linked to high densities and interaction patterns. As the project progresses, you will iteratively test and refine these models, comparing their outputs with observations from XR experiments and pilot city sensor networks, analysing model failures, and improving the transparency and interpretability of the predictions for policy use. In later project phases, you will validate the models using pilot generated data and ensure their suitability for real world decision making.

You will also contribute to the development of the SAFE MOVE integrated data platform—an expansion of the existing UMO platform—ensuring it can ingest heterogeneous data types generated in the SAFE MOVE pilots. Throughout the project, you will collaborate closely with European partners, support API based deployment of model outputs, and help translate analytical results into actionable insights for urban mobility planning and safety interventions, in particular for the municipality of Amsterdam.

The research environment

The PhD project is conducted at the Department of Transport and Planning (T&P) of Delft University of Technology. T&P aims at top-level fundamental research that contributes to a more efficient and robust design and reliable operation of transport systems. T&P is composed of 12 research labs addressing various transport challenges.

You will be part of the Mobility in eXtended Reality Lab (MXR Lab) as well as the Urban Mobility Observatory (UMO) lab. The MXR Lab conducts research on mobility behaviour by using controlled virtual environments to study how pedestrians, cyclists, vehicles, and mobility service users navigate spaces, respond to infrastructure, interact with technology, and behave in both every day and safety critical situations. The UMO lab aims to design data collection systems and supporting methods such as sensor network design, experimental design, sensor validation, and data fusion.

Job requirements

The candidate we are looking for has:

• An MSc degree or equivalent in data science or computer science.
• Strong analytical and programming skills (e.g., Python) with proficiency in statistics and machine learning.
• Experience with or a strong interest in road safety, mobility, or transportation research.
• Excellent communication and writing skills in English and the ability to collaborate with diverse stakeholders.
• It is a plus if you have experience in machine learning or developing a data platform.

TU Delft (Delft University of Technology)

Delft University of Technology is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society.

Faculty of Civil Engineering and Geosciences

The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Its research feeds into educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Research projects are conducted in close cooperation with a wide range of research institutions. CEG supports its scientists in integrating open science in their research practice.

Employment conditions

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5 year contract with an official go/no go progress assessment within 15 months, followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

#EUfunded This is an EU funded project, named SAFE MOVE, within program HE.