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
- Multimodal Segmentation and Tracking of Melanoma Lesions in Longitudinal PET/CT Scans
- Archive Retrieval & LLM Readiness (Fribourg Patois)
- Semantic Audio Segmentation for Archive Transcription
- Causal-based Multimodal Diffusion Models for Medical Image Analysis
- Radiation Experiments for in-Space Fault Tolerant DNNs
- Efficient Diffusion Models via Quantization and Compression
- Digging into the Transformers of 3D Foundation Model
- 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
- Modeling In-Orbit Radiation for Fault-Injection
- 3D Surface reconstruction using 3D Gaussians and SDF
- Spacecraft pose estimation, on the edge
- Multi-Video Feed Analysis Using Applied Deep Learning: Enhancing Recycling Efficiency through Comparative Object Detection and Classification at Sorting Machine Stations (CVLab x Wasteflow)
- 3D Medical Image Analysis beyond Voxels
- 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)
- Applied Deep Learning for Computer Aided Engineering
- Stacked objects: counting, segmenting, reconstructing
Semester Projects (Bachelor and Master)
- Generalizing a Pose Estimation Pipeline Beyond Spacecraft Dataset
- Stacked objects: counting, segmenting, reconstructing
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
Multimodal Segmentation and Tracking of Melanoma Lesions in Longitudinal PET/CT Scans
Understanding disease progression is critical for monitoring and predicting outcomes of immunotherapy in metastatic cancers. While immune checkpoint inhibitors (ICIs) have shown clinical success, many patients experience limited benefit or severe adverse effects. Accurate tracking of lesion progression and detection of new lesions is therefore essential. In the LETITIA project, we analyze longitudinal PET and (…)
Archive Retrieval & LLM Readiness (Fribourg Patois)
This project aims to bridge the gap between Fribourg’s oral heritage and modern technology. By leveraging the BCU’s extensive archives and a 40,000-entry dictionary, we are exploring how to retrieve hidden dialect content and assess its potential for training digital language models.
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.
Generalizing a Pose Estimation Pipeline Beyond Spacecraft Dataset
In a recent work, we developed a real-time pipeline for pose estimation of spacecraft from single images. A key contribution was a new mathematical formulation that significantly improved accuracy and led to a WACV 2026 publication.
Radiation Experiments for in-Space Fault Tolerant DNNs
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.
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 (…)
Digging into the Transformers of 3D Foundation Model
Recently, the task of 3D reconstruction has been revolutionized with the advent of VGGT (https://vgg-t.github.io/), a 3D foundation model that predicts scene geometry and camera parameters with only a transformer-based feed-forward network. However, transformers are known to be expensive to use, especially when the input consists of hundreds of images. This project aims to dig (…)