Job offer

The PhD project aims to develop a personalized and environment-sensitive walking model capable of describing how urban morphology influences pedestrian movement at the step level. Using a multi-sensor experimental platform combining inertial sensors, GNSS, camera and eye-tracking, the doctoral candidate will collect and analyze motion data in real urban environments.

The research will focus on extracting individualized walking signatures from sensor data, detecting locomotor events, and modeling the influence of environmental characteristics such as slope, street layout, intersections or visual stimuli. The work will combine signal processing, machine learning and sensor fusion techniques to build a multi-scale model linking micro-locomotor dynamics to urban morphology.

The PhD will be conducted at the GEOLOC laboratory (Université Gustave Eiffel) in collaboration with the AAU team at École Centrale de Nantes.

Profile: MSc in Computer Science, Robotics, Signal Processing, Biomechanics, Artificial Intelligence or related fields. Experience in Python, data analysis, machine learning or inertial sensors is appreciated., Master Degree or equivalent, We are looking for a candidate who is highly motivated by interdisciplinary research and interested in working at the intersection of signal processing, artificial intelligence, biomechanics, and urban mobility.

Education: Master's degree (or equivalent) in navigation/geomatics, signal processing/data science, computer science/artificial intelligence, biomechanics, or a related discipline. Excellent academic performance is expected.

Scientific and technical skills

• Solid foundation in signal processing (time/frequency analysis, filtering, event detection)
• Knowledge of machine learning (classification, supervised/unsupervised models)
• Programming skills (Python essential, C++ appreciated)
• Interest in multi-sensor systems and experimental data
• Previous experience in one of the following areas would be an asset: analysis of data from inertial sensors (IMU), human activity recognition, biomechanics of walking, sensor fusion, and computer vision

Personal skills

• Interest in experimental work in real-world conditions
• Ability to work in an interdisciplinary team
• Initiative, autonomy, and scientific rigor
• Good writing skills in English

Languages ENGLISH

• Flexible working hours
• International environment
• Subsidized meals
• Discounted public transportation
• Cultural and sporting activities

Additional comments