Engineering Jobs in Delft, Netherlands (Now Hiring) — 89 open — Page 4

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 SAFEMOVE 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 PhD project is conducted at the Department of Transport and Planning (T&P) of the 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 (e.g., sensor network design, experimental design, sensor validation, 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.

Conditions of employment 
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. 

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.  

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.  

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.  

Additional information
For more information, please contact Dr.Yan Feng ([email protected]).      

#EUfunded This is an EU funded project, named SAFE MOVE, with project number 101319217, within program HE.

Application procedure
Are you interested in this vacancy? Please apply no later than 19 July 2026 via the application button and upload the following documents:

• CV (maximum 2 pages).
• Motivational letter (maximum 2 pages).
• One-page summary of your MSc thesis.
• A writing sample, such as a chapter from your Master’s Thesis, demonstrating your research methodology and/or data analysis (e.g., Methods and Results sections); or a (forthcoming or published) article or presented conference paper.
• Transcripts of academic qualifications (BSc and MSc), including list of courses and marks.
• A three-slide presentation (PDF) with your initial research ideas and directions for this PhD project.

You can address your application to Yan Feng.

The first round of interviews will be held by the end of July, the second round of interviews will be held early September.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements. 

Please note:

• You can apply online. We will not process applications sent by email and/or post. 
• As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
• Please do not contact us for unsolicited services.