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The lattice Boltzmann method (LBM) is a state-of-the-art computational fluid dynamics (CFD) model used to simulate fluid flow based on the solution of the Boltzmann equation. LBM has considerable advantages in solving low Mach number flows as compared to conventional Navier-Stokes solvers, mainly due to the locality and explicitness of operations. This results in a huge potential for massive parallel computing on modern distributed memory machines.
Adaptive mesh refinement (AMR) is a commonly employed computational technique in CFD to enhance the ratio between efficiency and accuracy of simulations by dynamically adjusting the resolution of the computational grid based on some local indicators of the flow. By adaptively refining the grid in regions of interest, e.g. shocks in compressible flows or interfaces in multiphase flows, AMR can provide high resolution where it is most needed, while reducing computational effort and memory footprints in regions where coarser resolution is sufficient.
The group recently developed a parallel AMR solver based on a finite-volume discrete velocity Boltzmann method (FV-DVBM). The solver is strictly conservative and showed promising results for compressible flows with moderate Mach numbers and discontinuities. The advantages of AMR, however, are not restricted to the regime of compressible flows. Therefore, the group is currently developing an AMR framework for general purpose kinetic models, including LBM.
The goal of this project is to incorporate a standard lattice Boltzmann model for isothermal, low Mach number flows (D2Q9, possibly D3Q27) into the AMR framework and to validate it with (2D, possibly 3D) test cases. For a master’s thesis, the scope shall be extended to include work on boundary conditions, complex geometries, as well as models for compressible, turbulent, or multiphase flows, depending on discussed preferences.
- Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Physics
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| This thesis develops an automated onboard waste quantification system for a maritime waste collection vessel, leveraging computer vision with continual learning and domain adaptation to replace manual counting of floating waste. Evaluated under real-world maritime conditions, the system aims to improve waste management in the South East Asian Sea. - Engineering and Technology
- Master Thesis
| diaxxo, a start-up from ETH Zürich, is transforming molecular diagnostics with an innovative Point-of-Care Polymerase Chain Reaction (PCR) device. Designed to accelerate and democratize access to diagnostic testing, our cutting-edge technology can be used across various fields, from human diagnostics to vet and food testing.
Our products are also tailored for use in developing countries and resource-limited settings, aiming to bring reliable diagnostics to every corner of the globe.
The company offers several projects and thesis opportunities focusing on interfacing computer and camera systems (e.g. controlling Camera Pi from ESP microcontrollers, and integrating hardware and software components to address design and automation challenges. - Chemical Engineering, Computer Hardware, Electrical Engineering, Manufacturing Engineering, Mechanical Engineering, Software Engineering
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| In this project, we focus on continuous and quantitative monitoring of activities of daily living (ADL) in SCI individuals with the goal of identifying cardiovascular events and PI-related risk behaviors.
ADLs specific to SCI patients and their lifestyles shall be discussed and narrowed down in the scope of this work, therefore an autonomous camera-based system is proposed to classify ADLs.
The Current work builds on a previous project where a SlowFast network [1] was trained to identify SCI-specific classes and we aim to further improve the classification and temporal resolution for transferring to wearables' time-series data. - Computer Vision, Health Information Systems (incl. Surveillance), Intelligent Robotics, Knowledge Representation and Machine Learning, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition
- Bachelor Thesis, Course Project, ETH for Development (ETH4D) (ETHZ), ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| The objective of this project is to synthesize monodisperse solutions of elongated silica nanoparticles following established recipes. The nanoparticles will be analyzed with electron microscopy and their mode structure will be studied in optical traps operated in high vacuum. - Chemical Engineering, Electrical and Electronic Engineering, Materials Engineering, Optical Physics, Physical Chemistry
- Master Thesis, Semester Project
| The repetitive and high-impact nature of the golf swing may contribute to lower spine degeneration and chronic low back pain. This project aims to analyze the biomechanical loading of the lumbar spine during the golf swings through advanced motion capture and modeling techniques. A high-fidelity golf simulator combined with a mobile phone-based motion capture system will be used to evaluate swing mechanics. In Part A, state-of-the-art pose estimation models will be tested for their accuracy in extracting 3D motion data from monocular videos. In part B, biomechanical analysis will integrate pose data into an individualized OpenSim model to estimate spinal joint reaction forces and muscle activity. The ultimate goal is to develop a smartphone-based tool capable of real-time swing analysis to provide insight into injury prevention and technique optimization for golfers. - Artificial Intelligence and Signal and Image Processing, Biomechanical Engineering
- Master Thesis
| Osteoarthritis (OA) presents a significant challenge in healthcare, necessitating innovative solutions to alleviate pain, enhance mobility. This thesis documents the research and development journey of an OA knee orthosis within the Spinal Cord and Artificial Intelligence Lab (SCAI-Lab) at ETH Zurich.
This thesis is a close collaboration between the ORTHO-TEAM Group and the SCAI-Lab at ETH Zurich. The collaboration offers a unique exchange of expertise and resources between industry and academia. Together, we aim to make meaningful progress in the field of and empower students to make valuable contributions to their academic pursuits.
- Biomechanics, Biomedical Engineering
- ETH Zurich (ETHZ), Master Thesis
| Our goal is to establish a heterocellular 3D printed bone organoid model comprising all major bone cell types (osteoblasts, osteocytes, osteoclasts) to recapitulate bone remodeling units in an in vitro system. The organoids will be produced with the human cells, as they could represent human pathophysiology better than animal models, and eventually could replace them. These in vitro models could be used in the advancement of next-generation personalised treatment strategies. Our tools are different kinds of 3D bioprinting platforms, bio-ink formulations, hydrogels, mol-bioassays, and time-lapsed image processing of micro-CT scans. - Biomaterials, Biomechanical Engineering, Cell Development (incl. Cell Division and Apoptosis), Cellular Interactions (incl. Adhesion, Matrix, Cell Wall), Polymers
- Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| Design and build dexterous human-like robotic hands with us at the Soft Robotics Lab and the ETH spin-off mimic. We will explore different possibilities of developing design features and sub-systems. The developed features shall be integrated into a fully functional robotic hand and applied to solve practical manipulation challenges. - Electrical Engineering, Mechanical Engineering
- Master Thesis, Semester Project
| This project explores the sustainable design of advanced catalysts for environmental applications, ranging from gas sensors to catalytic systems for pollutant degradation and clean energy production. Emphasizing atom-efficient materials such as single-atom catalysts and nanostructured metal oxides, the research integrates scalable synthesis techniques with nanoscale engineering to enhance activity, selectivity, and durability. These materials will be tailored for applications including air quality monitoring, electrocatalytic hydrogen production, and catalytic removal of environmental toxins. The project aims to build foundational insights that support real-world deployment of green technologies addressing pressing environmental challenges. - Chemical Engineering, Inorganic Chemistry, Interdisciplinary Engineering, Materials Engineering, Mechanical and Industrial Engineering, Physical Chemistry
- Bachelor Thesis, Internship, Master Thesis, Semester Project
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