Thesis: Multimodal Fleet Intelligence for Decision Support

Volvo Group; Göteborgs kommun; On-site; SEK 134,000 - 201,000; Part time

Job summary

Volvo Group’s Volvo Autonomous Solutions is seeking a master’s thesis student to investigate how shared information from multiple vehicles can be used to detect changing site conditions and support operational decision-making across a fleet.

You will work with multi-modal vehicle data, CAN signals, maps and events, applying Python-based deep learning tools (PyTorch or TensorFlow) under expert supervision.

Qualifications

• Enrolled in a master’s programme in computer science, data science, machine learning, electrical engineering, robotics or related field.
• Strong Python programming skills and hands-on experience with PyTorch or TensorFlow.
• Interested in autonomous systems, machine perception, spatiotemporal modelling and real-world sensor data.
• Independent, structured and curious; analytical and motivated to solve technical problems.
• Proficient in written and spoken English.

Responsibilities

• Develop and compare approaches for modelling changing site conditions over time.
• Combine information from vehicle sensors, CAN signals, maps and events.
• Demonstrate evidence-grounded recommendations for fleet-level decision support.
• Measure detection quality, lead time, false alarms and robustness to missing sensors.
• Evaluate generalisation across vehicles and operating conditions.
• Document results in a scientific report.

Skills

Python

PyTorch

TensorFlow

English

Education

Master's program in CS/DS/ML/EE/Robotics

Tools

CUDA

Job description

Volvo Group’s Volvo Autonomous Solutions is seeking a master’s thesis student to investigate how shared information from multiple vehicles can be used to detect changing site conditions and support operational decision-making across a fleet.

You will work with multi-modal vehicle data, CAN signals, maps and events, applying Python-based deep learning tools (PyTorch or TensorFlow) under expert supervision.