Bachelor, Master and Doctoral School Projects

Through this project, you will be developing a solution for a real-world application that will be used in the future within WasteFlow service and will help optimize recycling.

Through this project, you will be developing a solution for real-world application that will be used in the future within WasteFlow service and will help optimize recycling. 

This project has the objective to explore the use of infrared, thermal, depth, and RGB data for the problem of detecting and classifying an object or a group of objects as dangerous in the case of partially or fully hidden objects.

Five instances of Liver extracted from MRI data. The black dot indicates a pseudo key point.In this project, we plan to explore methods to match/align shapes extracted from biomedical images, based on shape and texture-based features. By doing so, we will be able to analyze the variations of these structures over time and compare them (…)

The goal of this project is to develop a tool that allows the insertion of realistic synthetic observations of space objects into astronomical images.

In this project, the student will construct a number of 3D spacecraft and debris objects, integrate a number tumble profiles, and insert these motions into various earth orbits. The student will then render images of the objects in orbit to create a public dataset.

As described by Lionel Esparza, French musicologist, musician and radio producer (France Musique) in 2021, classical music is in decline. The reasons are various such as ageing of the audience, cultural shift and mismatch. At the same period, two major observations arose from the discussions between the Verbier classical music festival and the EPFL+ECAL Lab: (…)

Deep learning based approaches can be used for a wide range of space applications such as on-board data processing for observation satellite and collision prevention, spacecraft rendezvous, etc. Unfortunately, the deep learning models are very computational intensive and require huge amount of resources and power consumption. In recent years, some techniques like quantization, pruning and (…)

Computer Aided Engineering (CAE) is at the core of modern industrial engineering and manufacturing. However, the current CAE applications suffer from significant time and human resource expenses. Our goal is to leverage deep learning techniques to automate the CAE process and reduce the R&D costs for the industry.

Invision system tracking and geo-localizing passengers in a train station using 8 cameras. p { line-height: 115%; text-align: left; orphans: 2; widows: 2; margin-bottom: 0.25cm; direction: ltr; background: transparent }a:link { color: #000080; so-language: zxx; text-decoration: underline } Invision AI develops a multi-camera system to track people and cars. The current system sometimes mixes up persons (…)

An active research field is the prediction of physics mechanisms degrading plasma confinement and performance, eventually leading to disruptions on Tokamaks. Disruptions are the ultimate consequence of highly coupled non-linear plasma physics processes, resulting in an abrupt loss of plasma current and confinement inducing huge electromagnetic forces and thermal loads on Plasma Facing Components and (…)

As in many areas of computer vision, deep networks now deliver state-of-the-art results for delineation tasks, such as finding axons and dendrites in 3D light microscopy images. Most of the existing approaches rely on convolutional networks to extract from images binary masks denoting which voxels belong to neurites and which do not. Unfortunately, they do (…)

While modeling people wearing tight-fitting clothes is fast becoming a mature field, handling subjects wearing looser garments remains an open problem when it is to be done in everyday settings where people may hide each other, precise outlines are hard to estimate, and shadows often complicate matters. Our goal is therefore to develop robust and (…)

DescriptionDifferent visual tasks are often strongly and obviously correlated. For instance, having surface normals simplifies estimating the depth of an image, knowing segmentation could help detect objects, etc. Our intuition implies the existence of a special structure among visual tasks. Extracting this structure would allow us to seamlessly reuse supervision among related tasks or solve (…)

Many practical continuous minimization problems, such as aerodynamic optimization, are not amenable to gradient-based optimization methods because derivatives cannot be computed directly. We recently showed that it is possible to train a Neural Network regressor as a proxy to the numerical simulator and optimize the proxy function via Gradient-Descent. [ICML 2018 Paper]. For example, we (…)