University of Zurich

Open Opportunities

Adversarial Robustness in Event-Based Neural Networks University of Zurich Robotics and Perception The project will focus on studying various neural network architectures for event-based inference datasets and evaluate their performance in the presence of adversarial attacks. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Generating High-Speed Video with Event Cameras University of Zurich Robotics and Perception Event cameras have shown amazing capabilities in slowing down video as was shown in our previous work, TimeLens (https://www.youtube.com/watch?v=dVLyia-ezvo). In this project we want to push the limits of what is possible using such a method and explore new extensions. Computer Vision Master Thesis, Semester Project

Learning an Event Camera University of Zurich Robotics and Perception Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with a lot of potential for high-speed and high dynamic range robotic applications. They have been successfully applied in many applications, such as high speed video and high speed visual odometry. In spite of this success, the exact operating principle of event cameras, that is, how events are generated from a given visual signal and how noise is generated, is not well understood. In his work we want to explore new techniques for modelling the generation of events in an event camera, which would have wide implications for existing techniques. Applicants should have a background in C++ programming and low-level vision. In addition, familiarity with learning frameworks such as pytorch or tensorflow are required. Intelligent Robotics, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling Internship, Master Thesis, Semester Project

Asynchronous Processing for Event-based Deep Learning University of Zurich Robotics and Perception The goal of this project is explore ways to adapt existing deep learning algorithms to handle sparse asynchronous data from events. Engineering and Technology Master Thesis, Semester Project

Data-driven Event Generation from Images University of Zurich Robotics and Perception In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project. Computer Vision Master Thesis, Semester Project

Neural-based scene reconstruction and synthesis using event cameras University of Zurich Robotics and Perception The project will focus on exploring the use of event-based cameras in neural-based scene reconstruction and synthesis, extending available approaches to event-based data. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Data-driven Keypoint Extractor for Event Data University of Zurich Robotics and Perception The project aims to develop a data-driven keypoint extractor, which computes interest points for event camera data. Based on a previous student project (submitted to CVPR23), the approach will leverage neural network architectures to extract and describe keypoints in an event stream. Computer Vision Master Thesis, Semester Project

Learned Low-Level Controller University of Zurich Robotics and Perception Neural networks are renowned for their expressiveness. In this project, we study their application as a low-level controller on a drone. Engineering and Technology Master Thesis

End-to-End Learned Vision-Based Navigation University of Zurich Robotics and Perception Human pilots are very good at drone racing, even though they only use images as input. In this project, we aim to develop a neural network that can be used to autonomously fly through a sequence of gates while using the same visual information that is also provided to human pilots. Engineering and Technology Master Thesis

Localization techniques for drone racing University of Zurich Robotics and Perception Benchmark comparison of localization techniques. Engineering and Technology Master Thesis