ETH Competence Center - ETH AI Center

Open Opportunities

Closing Sim-to-Real Visual Domain Gap for Transparent and Reflective Objects ETH Zurich Robotic Systems Lab Other organizations: ETH Competence Center - ETH AI Center Robots need to manipulate a wide range of unknown objects, from transparent to shiny surfaces. The goal of this project is to investigate learning techniques to bridge the visual domain gap between high-fidelity rendered scenes and real-world images for scene understanding. Computer Vision, Intelligent Robotics, Knowledge Representation and Machine Learning Master Thesis

Learning Object Manipulation from Demonstrations using Tactile Feedback and Imitation Learning ETH Zurich Robotic Systems Lab Other organizations: ETH Competence Center - ETH AI Center Recently, there has been significant progress in learning object manipulation from human videos. One of the key limitations of these methods is the absence of tactile feedback, making it hard to identify whether or not a contact has been made. Thus, in this project, we would like to investigate how we can use demonstrations including tactile measurements to learn object manipulation. Intelligent Robotics, Mechanical Engineering Master Thesis

Machine Learning-Based Automated Analysis of Murine Brain Corrosion Casts University of Zurich Bjoern Menze Other organizations: ETH Competence Center - ETH AI Center This project aims to investigate the use of machine learning-based algorithms to obtain a deeper understanding of the graph-structured vasculature preserved in corrosion casts. For a more detailed description, please refer to the attached document. Central Nervous System, Computer Vision, Neural Networks, Genetic Alogrithms and Fuzzy Logic Lab Practice, Master Thesis, Semester Project

Parametrized Shape Optimization using Surrogate Fluid Models ETH Zurich ETH Competence Center - ETH AI Center Fast and efficient structure optimization based on parametrized shapes in surrogate fluid simulation environment. Engineering and Technology, Information, Computing and Communication Sciences, Physics Bachelor Thesis, Master Thesis, Semester Project

Modeling GPR data via physics-informed Neural Networks ETH Zurich ETH Competence Center - ETH AI Center Other organizations: Structural Mechanics (Prof. Chatzi) This master thesis explores the use of Neural Networks (NNs) to model Ground Penetrating Radar (GPR) data. To this end, Physics-Informed Neural Networks (PINNs) can be used to incorporate electromagnetic wave propagation into the NN training in order to enhance the learning task. The inferred deep learning model can be used for fast simulation of GPR data for decision support tasks within asset management of the railway infrastructure. Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Physics Master Thesis