Doctoral School Projects
- Constraint-Compliant Controllable Models
- Composites Engineering Design through Deep Geometric Learning
- Structured 3D Shape Optimization with Part-Based Implicit Neural Representations
Master Thesis or Doctoral School Projects
- Applied Deep Learning for Computer Aided Engineering
- Proton Irradiation of Nvidia GPU
- Real-Time 3D Tracking and Anomaly Detection for Maritime Kite Systems
- Archive Retrieval & LLM Readiness (Fribourg Patois)
- Semantic Audio Segmentation for Archive Transcription
- Efficient Diffusion Models via Quantization and Compression
- AI Meets Art History: Generating the Missing Part of the Statue at Lausanne’s Cathedral with 3D Generative Models
- Towards Generalizable Embodied AI via Diffusion-Based Robotic Policies and GPT-Powered Scene Generation
- 3D Surface reconstruction using 3D Gaussians and SDF
- Multi-Video Feed Analysis Using Applied Deep Learning: Enhancing Recycling Efficiency through Comparative Object Detection and Classification at Sorting Machine Stations (CVLab x Wasteflow)
- Waste Detection in real-time: Object detection of waste type from the fall of waste in a waste cell (CVLab x Wasteflow)
- Object detection and classification of waste type and mass assessment as it moves along a conveyor belt (CVLab x Wasteflow)
- Stacked objects: counting, segmenting, reconstructing
Semester Projects (Bachelor and Master)
Please note that most of the offered semester projects can be reformulated for a thesis and vice versa. Please contact us directly.
For further project offers, please contact members of the CVLAB directly.
Job Offers
None available at the moment.
Administrative
Semester Projects (Bachelor and Master)
SIN and SSC students do one semester project during their Bachelor studies and one semester project during their Master studies.
Semester projects can be done in groups of two students.
Semester projects are worth 8 credits for Bachelor and 12 credits for Master.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
Oral defense: within two weeks of the hand-in date.
Master Thesis Projects
Master Thesis Projects are started once the complete master program is finished and all the credits have been obtained.
Projects for SSC and SIN students should last 4 months at the EPFL or 6 months in the industry or in another University.
Master Thesis Projects must be done individually.
Master Thesis Projects are worth 30 credits.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
Additional information
Proton Irradiation of Nvidia GPU
CVLab and the Space Centre are currently engaged in research supporting deploying machine learning algorithms on edge devices in-orbit. This includes power constraints, bandwidth constraints, explainable AI, and improving fault-tolerance of machine learning algorithms.
Real-Time 3D Tracking and Anomaly Detection for Maritime Kite Systems
At Aether Swiss Kite, we are developing an automated, plug-and-play kite wing system designed to decarbonize the maritime industry. To ensure the system’s safety and reliability, we are implementing a 360° vision- based redundancy system for real-time kite tracking. Building upon a previous semester project that achieved reliable 2D detection, trajectory mapping, and orientation tracking, (…)
Semantic Audio Segmentation for Archive Transcription
Ahead of its 2026 reopening, the Cantonal University Library of the Canton of Fribourg is partnering with the SDSC and EPFL to transform its digital archives. This project aims to implement semantic audio segmentation, moving away from fixed time intervals to allow for deeper, more coherent exploration of Fribourg’s rich cultural heritage.
Semantic Audio Segmentation for Archive Transcription
Ahead of its 2026 reopening, the Cantonal University Library of the Canton of Fribourg is partnering with the SDSC and EPFL to transform its digital archives. This project aims to implement semantic audio segmentation, moving away from fixed time intervals to allow for deeper, more coherent exploration of Fribourg’s rich cultural heritage.
Stacked objects: counting, segmenting, reconstructing
In a recently published work [link: https://corentindumery.github.io/projects/stacks.html ], we have developed and released a dataset of stacked objects. It is a particularly challenging setting for many computer vision or robotics tasks, and we would like to take things further and use this dataset in new ways. There are many potential follow-up works that could lead to publications (…)
Towards Generalizable Embodied AI via Diffusion-Based Robotic Policies and GPT-Powered Scene Generation
Imagine a robot that not only follows instructions, but also autonomously decomposes complex goals, perceives the world, and takes actions like a human. This would mark a significant step toward general-purpose embodied intelligence, with applications in autonomous exploration in space, personal assistance, industrial automation, and beyond. This project offers the opportunity to work on frontier (…)
Efficient Diffusion Models via Quantization and Compression
Diffusion models, such as Stable Diffusion, have achieved state-of-the-art results in generative image synthesis. However, they require hundreds of millions (or even billions) of parameters, making them computationally expensive and memory-heavy. This hinders deployment on resource-constrained devices like mobile phones or edge accelerators. Objective: Explore and systematically evaluate quantization and compression methods tailored for diffusion (…)
AI Meets Art History: Generating the Missing Part of the Statue at Lausanne’s Cathedral with 3D Generative Models
Over the decades and centuries, many statues displayed in cultural monuments have been damaged. The goal of this project therefore is to study the use of the recent developments in Diffusion Model, Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) to digitally reconstruct in 3D the head of the Virgin with the Child statue (…)
Multi-Video Feed Analysis Using Applied Deep Learning: Enhancing Recycling Efficiency through Comparative Object Detection and Classification at Sorting Machine Stations
Every year we generate globally 2 billion tonnes of waste and WorldBank forecast that this number could rise up to 3.4 billion tonnes in 2050. To transform this matter into valuable resources, we need to tackle the problem of efficient recycling. The recycling process comprises multiple steps from waste collection to material transformation, however the (…)