Master Thesis Intern

The Computer Vision and Machine Learning team at EA SPORTS TRACAB in Stockholm offers master's thesis projects for Spring 2027. The team develops computer vision and machine learning technology for capturing, reconstructing, and understanding sports. Students work closely with engineers on challenging, open-ended research problems using real-world sports data.

• Work closely with engineers on challenging, open-ended research problems using real-world sports data.
• Agree the precise scope and methodology with the team, adapting to background, interests, and research ideas.
• Develop a rigorous evaluation framework and compare modern machine learning and foundation-model approaches with strong statistical and machine learning baselines for intelligent quality assessment of sports tracking data.
• Investigate modern machine learning approaches for automatically recognising football events from visual data, player and ball tracking data, or combinations of the two.
• Work with real football footage, accurate tracking data, and corresponding event annotations, exploring vision-language models and models that reason over multivariate spatiotemporal player and ball trajectories.
• Investigate state-of-the-art 3D human reconstruction methods using multi-view footage from close-contact sports, establishing capabilities and limitations and improving reconstruction accuracy and temporal consistency during severe occlusion and physical interaction.
• Investigate how learning-based techniques can complement geometric calibration and numerical optimisation using real multi-camera sports data.
• Investigate machine learning methods that combine visual evidence, temporal modelling, and physical constraints to produce accurate and robust 3D ball trajectories, including representing uncertainty during uncertain or missing observations.
• Investigate computer vision and machine learning methods for estimating the position and orientation of the ball throughout American football play, exploiting spatial context, temporal information
• And observations from multiple cameras to maintain reliable estimates through challenging game situations.

• Current master's student eligible to undertake a master's thesis project.
• Interest in one or more of the offered thesis projects.

• Interest in foundation models, anomaly detection, time-series learning, and explainable AI.
• Interest in vision-language models, video understanding, temporal modelling, and multimodal learning.
• Interest in cutting-edge computer vision, human motion understanding, multi-view learning, and 3D reconstruction.
• Enjoyment of combining modern machine learning with multi-view geometry, mathematical modelling, and optimisation.
• Interest in computer vision, sequence modelling, probabilistic inference, and physics-informed machine learning.
• Interest in occlusion-aware ball tracking and pose estimation in American football.