PhD: Automated Mobility & Traffic Modelling (Fully Funded)

KU Leuven; Belgique; Sur place; EUR 45 000 - 60 000; Plein temps

Résumé du poste

KU Leuven in Belgium seeks a highly motivated candidate for a four-year, fully funded PhD position at the intersection of automated mobility, traffic modelling, and control design. You will study how automated vehicles interact with human drivers in mixed traffic and develop driving strategies that balance safety and efficiency.

The project combines modelling, data analysis and collaboration with public transport operators and insurers, offering hands-on data work, real pilots such as autonomous

Qualifications

• a master's degree (with at least distinction) in engineering, computer science, physics, applied mathematics, or a related field.
• Strong interest in applying quantitative and mathematical methods to real-world mobility problems.
• Programming skills (e.g. Python or similar).
• Excellent command of English, both written and spoken.
• Background in traffic flow theory, transport engineering, or control systems.
• Experience with data analysis, optimisation, or simulation.
• Interest in automated/connected vehicles and mobility systems.

Responsabilités

• Develop methods to design and evaluate driving strategies for automated mobility.
• Combine traffic modelling, data-driven analysis, and control design.
• Collaborate with stakeholders such as public transport operators and insurers.
• Bridge theory and practice using real data and pilots like autonomous buses.

Connaissances

Programming skills (Python)

Quantitative methods

English proficiency

Formation

Master's degree (with distinction) in engineering/computer science/physics/applied mathematics

Outils

Python

Description du poste

KU Leuven in Belgium seeks a highly motivated candidate for a four-year, fully funded PhD position at the intersection of automated mobility, traffic modelling, and control design. You will study how automated vehicles interact with human drivers in mixed traffic and develop driving strategies that balance safety and efficiency.

The project combines modelling, data analysis and collaboration with public transport operators and insurers, offering hands-on data work, real pilots such as autonomous