BACKGROUND & RESEARCH PROBLEM

Wheel loaders and other construction machines operate on rough, unstructured and dynamic terrain where the ground is deformable, obstacles appear and disappear, and people and vehicles move nearby. Navigating such terrain safely requires reasoning about where a given machine can go and tightly coupling that reasoning with the control system. This project develops methods for safe navigation on rough and dynamic terrain, such as a construction site, for a wheel loader.

OBJECTIVES

Industry: Safe navigation on rough and dynamic terrain, such as a construction site, for a wheel loader.

Science

· Develop methods for building traversability costs from real-time sensor data for a given machine and sensor modality.

· Develop a framework for the rigorous and efficient integration of traversability costs into safety controllers.

Training: Broaden knowledge of the state of the art in related fields such as model predictive control, control barrier certificates, real-time 3D sensing and mapping, and ML-based sensing and control.

YOUR PROFILE

· MSc in computer science, robotics, electrical engineering, applied mathematics, machine learning or a related field.

· Strong programming skills (Python and/or C++) and a solid mathematical foundation.

· Experience with one or more of: 3D perception, SLAM, computer vision, control theory or machine learning.

· Independent, curious and rigorous, with excellent written and spoken English.

WHAT WE OFFER

· A fully funded doctoral position working on a real industrial problem with access to unique field data from construction sites.

· Co-supervision from academia and an experienced Volvo autonomy/perception team.

· Compute resources (GPU clusters) and access to test machines and sensor platforms.

· A collaborative research environment with the ambition to publish at leading venues.

SCOPE & APPLICATION

Industrial PhD position, approx. 4-5 years, based in Gothenburg, Sweden, with time split between the university and Volvo. To apply, send your CV, grade transcript and a short motivation letter. Scope and start date are agreed together with the supervisors.

As part of the recruitment process, a background check as well as alcohol and drug testing may be conducted. You will be informed if applicable.