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

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?

You will work in the SAFE MOVE project on advanced data integration and AI‑driven safety analytics for micromobility. The data and models developed enable researchers and cities to understand where and why micromobility risks appear, 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—to build integrated risk models. Your responsibilities include: fusing heterogeneous data streams into a coherent risk representation, designing algorithms to detect safety risks and unsafe behaviours, predicting emerging hazards linked to high densities and interaction patterns, iteratively testing and refining models against XR experiments and pilot city sensor networks, analysing model failures, and improving transparency and interpretability for policy use.

In later project phases you will validate the models using pilot‑generated data, ensure their suitability for real‑world decision making, and contribute to the development of the SAFE MOVE integrated data platform to ingest heterogeneous data types generated in the pilots. You will also 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, particularly for the municipality of Amsterdam.

• 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.

Doctoral candidates will be offered a 4‑year period of employment divided into two contracts: an initial 1.5‑year contract with an official go/no‑go progress assessment within 15 months, followed by an additional 2.5‑year contract contingent on satisfactory performance.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, ranging from €3,059 to €3,881 gross per month across the four years of a full‑time (38‑hour) contract, plus 8 % holiday allowance and an end‑of‑year bonus of 8.3 %. You will also receive the TU Delft Graduate School enrolment, a customisable compensation package, discounts on health insurance, a monthly work‑costs contribution, and flexible work schedules.

For more information, please contact Dr. Yan Feng (y.feng). This EU‑funded project (SAFE MOVE, project number ) is part of the Horizon Europe programme.

Final date to receive applications: 19 Jul 2026.