ETH Competence Center - ETH AI Center

Open Opportunities

Generative AI for Synthesizing Robotic Tasks ETH Zurich Robotic Systems Lab Other organizations: ETH Competence Center - ETH AI Center Achieving task-level generalizations requires acquiring a large amount of rich interaction data. Simulators offer a safe, efficient, and cost-effective means of system development. However, generating task and scene-level diversity requires significant human effort to develop and verify novel tasks. This project aims to automate this process by leveraging the grounding capabilities of large language models. Intelligent Robotics, Knowledge Representation and Machine Learning Bachelor Thesis, Master Thesis, Semester Project

Machine Learning based biosensor readout analysis ETH Zurich ETH Competence Center - ETH AI Center What if a simple smartphone app using artificial intelligence could help enhancing the understanding of one’s health and monitor with high precision the presence and evolution of important biomarkers for various physiological and pathological aspects? This project aims to develop an artificial intelligence-based application that enables real time and high precision quantification of biomarkers in body fluids, from an image of an assay’s readout. To do so, computer vision techniques will be applied, and different neural network will be tailored for the desired application to enhance the potential of a paper-based point of care device. Artificial Intelligence and Signal and Image Processing, Biomaterials, Medical and Health Sciences ETH Zurich (ETHZ), Internship, 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

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 and the information below. Central Nervous System, Computer Vision, Neural Networks, Genetic Alogrithms and Fuzzy Logic Lab Practice, Master Thesis, Semester Project

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