ETH Zurich

Open Opportunities

Error bounds for scalable Gaussian process regression ETH Zurich Research Zeilinger The goal of the project consists in deriving error bounds for the approximate Gaussian process regression method given by the FITC method. Engineering and Technology Master Thesis, Semester Project

Error bounds for Regularized Trigonometric Regression in the Multi-task setting ETH Zurich Research Zeilinger Multi-task learning is the problem of jointly learning multiple functions that are “related” to each other. By leveraging this similarity, estimation performance can be improved on each (possibly unseen) task, and one can make an efficient use of the available data. The project aims at deriving uncertainty bounds around the multi-task-system estimates. Specifically, the candidate will work with the regularized trigonometric regression inspired by the so-called sparse-spectrum Gaussian process regression, investigate the issue of bias learning (i.e., finding the features that encode similarity among tasks) and derive error bounds for it, possibly setting the analysis in the statistical learning framework. Engineering and Technology Master Thesis, Semester Project

Learning-based stochastic Model Predictive Control with scalable Gaussian process regression ETH Zurich Research Zeilinger One of the key ingredients in Model Predictive Control (MPC) schemes is an effective model of the dynamical system’s response to external inputs. However, first- principles models are often not accurate enough, as there might be unknown external disturbances and model mismatches. To address this, learning-based control aims at complementing nominal models with data-based ones, which can be refined online as new system observations are gathered. Thus, such a model should be both expressive and fast to update. This project focuses on a learning-based stochastic MPC scheme, where uncertainty in the model is learned with an approximate Gaussian process, namely the regularized trigonometric regression stemming from the so- called sparse-spectrum Gaussian processes. To this aim, the candidate will review the available uncertainty bounds around these approximate Gaussian-process-based estimates and incorporate them in the MPC formulation. The chance-constraints thereby obtained are then to be analyzed to rigorously prove recursive feasibility and stability of the closed-loop system. Engineering and Technology Master Thesis, Semester Project

Organ-on-Chip Automation Hardware Design: Development and Integration of a Miniature Robotic XY Stage for a Miniature Microscope ETH Zurich Sensory-Motor Systems Lab Organs-on-Chip (OoC) replicate human organs in vitro but often lack physiological perfusion profiles. This project aims to develop a compact robotic XY stage for integration with OoC perfusion systems, enhancing automation and precision. By improving compatibility and mimicking dynamic blood flow, this innovation advances research and pharmaceutical applications. Biomedical Engineering Master Thesis

Diffusion-based Shared Autonomy System for Telemanipulation ETH Zurich Robotic Systems Lab Robots may not be able to complete tasks fully autonomously in unstructured or unseen environments, however direct teleoperation from human operators may also be challenging due to the difficulty of providing full situational awareness to the operator as well as degradation in communication leading to the loss of control authority. This motivates the use of shared autonomy for assisting the operator thereby enhancing the performance during the task. In this project, we aim to develop a shared autonomy framework for teleoperation of manipulator arms, to assist non-expert users or in the presence of degraded communication. Imitation learning, such as diffusion models, have emerged as a popular and scalable approach for learning manipulation tasks [1, 2]. Additionally, recent works have combined this with partial diffusion to enable shared autonomy [3]. However, the tasks were restricted to simple 2D domains. In this project, we wish to extend previous work in the lab using diffusion-based imitation learning, to enable shared autonomy for non-expert users to complete unseen tasks or in degraded communication environments. Intelligent Robotics, Robotics and Mechatronics ETH Zurich (ETHZ), Semester Project

Instruction Space Characterization for Interactive Human-Robot Domains ETH Zurich Sensory-Motor Systems Lab Human intention drives goal-directed actions, requiring decomposition into hierarchical instructions, from abstract strategies to concrete interactions. Robotics seeks to automate these instructions, but challenges arise in domains like neurorehabilitation, where complex human-robot interactions introduce inefficiencies from incomplete automation, diverse user preferences for strategies, and difficulties to engage with technical parameters. This thesis will explore shared instruction space characteristics across robotic domains, focusing on bottlenecks in automating high-level instructions. Novel metrics will be developed that define and classify instruction layers, strategies, and requirements. Intelligent Robotics Bachelor Thesis

High dose high intensity proprioception training for neurological patients using a robotic device ETH Zurich Rehabilitation Engineering Lab Proprioception, often called the "sixth sense," is vital for coordinating movements and maintaining balance, especially in the hands and fingers. In neurological patients, impaired proprioception in the upper limbs can hinder daily tasks and reduce quality of life. Traditional rehabilitation often lacks the intensity and precision needed for optimal recovery of fine motor skills. This project leverages high-dosage training with the ETH MIKE, a validated one-degree-of-freedom robotic device designed for precise, repetitive movements to enhance proprioception and motor function. By promoting neuroplasticity and functional recovery, this research aims to advance rehabilitation practices. Participants will gain hands-on clinical experience, train with neurological patients, and collaborate with therapists, engineers, and researchers. Biomedical Engineering, Clinical Sciences, Human Movement and Sports Science, Medicine-general, Neurosciences, Other Bachelor Thesis, Internship, Master Thesis, Semester Project

Integrating Machine Learning Process and Thermodynamic Modeling for Separation Processes ETH Zurich Energy and Process Systems Engineering Laboratory Do you want to combine your knowledge of process modeling with machine learning and thermodynamic modeling? In this project, you will evaluate the efficiency and accuracy of ML-based adsorption process modeling compared to existing first-principle models. Adsorption separation processes are, e. g., required in the chemical industry or for carbon capture applications. For that, you will integrate adsorption calculations based on classical Density Functional Theory into the ML model. This integration enables the large-scale prediction of separation performance for many materials at the process level. Chemistry, Engineering and Technology, Information, Computing and Communication Sciences Bachelor Thesis, Semester Project

Learning to fly soft drones ETH Zurich Environmental Robotics Laboratory Advances in aerial robotics and autonomy have led to remarkable achievements, primarily through the use of drones with rigid frames. However, nature offers a different paradigm: animals’ wings are significantly softer than these rigid structures, yet they achieve highly efficient and agile flight. This project reimagines aerial drone design by focusing on soft, deformable frames and leveraging machine learning to control their unique dynamics. Intelligent Robotics, Robotics and Mechatronics Internship, Master Thesis

Trainee in Numerical Optimization of High Brightness Electron sources for FELs with the inclusion of intra-beam scattering (index no. 8414-T1) Paul Scherrer Institute Paul Scherrer Institute With the observation of intra-beam scattering (IBS) in the SwissFEL injector and new understanding in how to model IBS, the question is whether for future electron sources do we need to rethink optimization process which generally aims to reduce the beam emittance and increase the peak current. This project will investigate the use of IBS in modelling of electron source, using the TW gun under development at PSI as an example, to understand whether there is a regime where the optimization process must consider IBS. For example, how the combined effect of IBS and beam size affects the slice energy spread in the waist of the emittance compensation scheme. During your trainee work you will be integrated into a small team to conduct numerical simulations of beam dynamics including IBS using existing tools. Physics Internship