Are you a final-year master’s student looking for a thesis project that blends real-world impact with cutting-edge tech?
At Framna, we are passionate about creating digital products that shape markets and make everyday life better - and we want you to be part of it.
Every year, we welcome a group of master’s students into our Stockholm studio to spend their final semester with us. You will sit alongside our developers, designers, testers, analysts, and product delivery leads, get a place to sit in our cozy office, and join our social and learning activities - from lunch-and-learns to our weekly Friday demos and AWs. It is a chance to do your thesis in a collaborative setting, surrounded by people who are happy to share what they know.
Sports analytics is a rapidly growing field, and table tennis presents a particular challenge: its fast pace, small ball, and complex tactical patterns make automated video analysis both technically demanding and practically valuable.
Current approaches to sports video analysis are dominated by task-specific computer vision pipelines with specialised models for object tracking, pose estimation, and event detection. Now Vision-language models (VLMs) offer a less explored alternative. As language models, they can be asked open-ended questions about a scene without being trained for the task first. Whether that suits something as fast and data-heavy as match footage is untested.
Some potential tasks within this thesis include:
Implementing and evaluating a computer vision approach using tools such as OpenCV, MediaPipe, or YOLO-style object detection to analyse match footage including for example tracking, pose estimation, and event detection
Testing one or more vision-language models to analyse that same match footage to compare to the baseline of the computer vision approach
Testing one or more vision-language models to analyse match footage and extract tactical insights, player behaviour, and rally patterns
Exploring a hybrid approach that combines structured computer vision and multimodal AI interpretation
Manually annotating a small dataset to serve as ground truth for evaluation
Evaluating each approach on tasks such as rally segmentation, serve detection, shot rhythm, score tracking, and tactical pattern recognition
Presenting findings including a prototype analysis tool, a small annotated benchmark dataset, evaluation metrics, and design recommendations for future sports analysis apps
This thesis may suit you if you:
Are interested in computer vision, machine learning, or multimodal AI
Are curious about applying AI to sports analytics and real-world performance data
Enjoy working with video data and building evaluation pipelines
Plan to start your master's thesis project in January 2027
Have the opportunity to work from our Stockholm studio
Framna means ‘to bring forward’ in Old Norse. And that is exactly what we do. We partner with industry leaders (and those about to be) to create excellent digital products that define markets, reshape industries, and deliver meaningful impact.
Born from the union of digital agencies Bontouch, Move, and Shape, Framna emerged from a strong, product-led culture. Today, over 600 of us collaborate across ten studios in Denmark, the Netherlands, Poland, Sweden, Switzerland, and the US.
Every day, millions of users around the world rely on the products we help shape. From seamless payments to daily commutes, from better health management to smarter shopping. Our work supports more than €150 million in daily transactions and consistently earns an average App Store rating of 4.5.
Together with some of the world’s most ambitious brands, including Essity, SJ, Swish, and SEB, we craft products that lead markets and push boundaries. Because when products win, businesses win. We call it Win by product.
Do you want to meet the team in person? Join us on September 17th for our Master's Thesis Evening! You will hear more about our upcoming thesis topics, meet the team behind our projects, and get a feel for how we work at the intersection of creativity and technology. Sign up here!
