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Cellular materials have attracted massive research interest recently due to their excellent mechanical properties. Specifically, truss lattices constructed by periodic assembly of truss microstructures have shown great design freedom and significant tunability in their properties by manipulating the topology and geometry of microscale features. Recent advancements in additive manufacturing technology have largely facilitated the functional design of metamaterials across several length scales. In particular, the simplicity in numerical characterization and theorectical treatment of truss metamaterials has allowed us to explore the diverse property space in terms of a wide range of properties, including elasticity, energy absorption, and controllable nonlinear behavior, etc. Various optimization approach have been developed towards the design of truss lattices with tailored properties and functionalities. Despite all progress, key challenges persist in the inverse design: while investigating the properties of a given structure can be straightforward, identifying a candidate structure with target structures has relied on inefficient trial-and-error or classical optimization schemes that are often computationally expensive and infeasible in large-scale problems.
In light of these challenges, we aim to establish an inverse mapping between the structure topology and the dispersion relationship of truss metamaterials using generative modeling techniques in machine learning. Specifically, the dispersion relation is one of the physical basis of mechanical metamaterials and is essential for manipulating various properties, such as the bandgap. This project proposes the development of a computational design framework that realizes the generation of optimal structures for desired properties. This inverse problem is explored in a data-driven manner, e.g., to quantitatively understand appropriate architectures for a given dispersion relation. The framework will leverage (i) the in-house numerical tools to compute and analyze the dispersion relation of a diverse truss metamaterial database; (ii) machine learning techniques to explore the design space and quantitatively model the relationship between design parameters and structural properties. This efficient data-driven inverse design framework will significantly accelerate the design of metamaterials with tailored properties at minimum computation cost. The project is to some extent flexibly adjustable to your ideas and interests, e.g., proposed structures can be validated through experimental works.
- Engineering and Technology
- Bachelor Thesis, ETH Organization's Labels (ETHZ), Master Thesis, Semester Project
The reduction of CO2 emissions requires the use of CO2 to mitigate climate change. CO2 utilization to chemicals offers the advantage of mitigating climate change and replacing fossil-based chemicals. However, current Carbon Capture and Utilization (CCU) processes suffer from high energy demands and emissions. A promising approach to reducing emissions are Integrated Carbon Capture and Utilization (ICCU) processes. ICCU processes omit the energy-intensive CO2 desorption step from the solvent by converting the CO2 to a valuable product directly within the solvent. One approach is the use of amines and metal-organic frameworks (MOFs) for the chemical absorption of CO2 from the air and direct hydrogenation of the formed carbamate. However, this integrated process's individual steps and reactions are not yet fully understood. In this project, you will investigate the steps for an ICCU process using amines and MOFs. The use of kinetic models enhances the mechanistic understanding of molecular processes. The task involves the development of kinetic models, and the project is planned in close collaboration with experimental work at the Laboratory for Catalysis and Sustainability at the Paul Scherrer Institute. Within the project, you will have the opportunity set own priorities.
- Chemistry, Engineering and Technology
- Bachelor Thesis, Collaboration, Internship, Master Thesis, Semester Project
Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. The goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time.
- Computer Vision, Intelligent Robotics, Robotics and Mechatronics
- Master Thesis, Semester Project
How can knowledge regarding physical activity type impact the estimation of human energy expenditure using wearable sensors?
Human energy expenditure (EE) refers to the amount of energy an individual uses to maintain essential body functions (respiration, circulation, digestion) and because of physical activity. Knowledge regarding the expended energy or calories could help people (e.g., athletes, obese, diabetic) to plan their physical activity for leading a healthier lifestyle. Additionally, it could be used to enable nutrition coaching for weight management purposes. Devising methods for EE estimation is a key enabler of the mentioned intervention strategies and it is the core goal of this project.
In this project, we will use an existing dataset, which has been collected while participants performed different physical activities (e.g., cycling, running). The dataset contains sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist) and respiratory data collected with an indirect calorimeter as well as other information (e.g., demographics and body composition data, type and intensity of physical activity performed). We will develop state-of-the-art deep learning models to estimate human energy expenditure and recognize physical activity using sensor data. The student will be required to implement single-task and multi-task pipelines to investigate the performance of separate and joint learning of human energy expenditure and physical activity.
- Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis
Adapt a model of treatment of osteoporosis with denosumab, PTH, bisphosphonates or romosozumab to explore sequential and branching simulations and identify optimal sequential and combinatorial drug treatments
- Medical and Health Sciences
- Bachelor Thesis, Master Thesis, Semester Project
The late fetal and neonatal stage is critical for brain development. In the clinical practice, it is often very difficult to quantify any lag or acceleration compared to the normal developmental trajectory. In some disorders, such as in intrauterine growth retardation or congenital heart disorders, estimating the “developmental brain age” would be of critical practical importance. 3D ResNets have previously been used to evaluate brain age in elderly subjects mainly in the context of Alzheimer’s disease. In early brain development, their use is not well documented. In this project, the thesis student will implement a 3D ResNet or similar architecture that estimates brain developmental age on 3D MRI data.
- Biomedical Engineering, Computer Vision, Medical and Health Sciences
- Course Project, Internship, Lab Practice, Master Thesis, Semester Project
We are developing a novel imaging technique for the label-free real-time tracking of chemical gradients, as they occur in most convection-reactions systems. Being able to observe the transport of a chemical species in 2D and real-time has a large potential in many fields, from catalysis, to energy storage, to in-vitro diagnostics
- Chemical Engineering, Environmental Engineering, Fluid Physics, Materials Engineering, Mechanical and Industrial Engineering, Thermodynamics and Statistical Physics
- Master Thesis
The surface of the gastrointestinal (GI) tract is covered by a mucosal membrane, consisting of enormous health-related biochemical, physiologic, and pathophysiologic information, and serving for nutrition exchange. Progress has been made to access the GI mucosa for diagnostics and therapeutics in clinical settings. However, it is still extremely challenging to build a biocompatible and robust GI mucosa interface enabling real-time, continuous, and minimally invasive interactions with human body, due to the constant GI motility, fast cellular turnover rate, limited cavity space and extremely chemical and biological environments
In the Traverso Laboratories at Brigham and Women’s Hospital (Harvard Medical School), We are exploring novel engineering approaches to develop robust mucosal interfaces for long-term deployment of micro-electronics/drug reservoirs/physical barriers in the GI tract.
- Biomaterials, Biomechanical Engineering, Mechanical Engineering, Polymers
- Master Thesis
Wildfires happen quite frequently and share many similarities with nuclear accidents. Following a nuclear core meltdown and breach of the containment, radioactive material (fission products) are released to the environment, transporting in a plume, and affecting the neighboring communities with potentially carcinogenic radiations. Similarly, the moving wildfires generate toxic chemicals in different forms, which are transported by the winds, exposing the land and the population to many carcinogens such as polycyclic aromatic hydrocarbons, benzene, formaldehyde, phenols, and heavy metals. Moreover, both wildfires and radioactive releases trigger large-scale evacuations and social disruptions in the affected regions. Furthermore, areas contaminated by wildfires and nuclear releases require several years – and sometimes decades – to recover (e.g. decontamination vs reforestation/cleaning). This master’s thesis aims to compare the economic and socio-environmental impacts of nuclear accidents vis-à-vis wildfires.
- Engineering and Technology
- Master Thesis
This project aims to develop algorithms to reconstruct and analyze multispectral optoacoustic tomography (MSOT) images from patients with carotid artery plaques.
- Biomedical Engineering, Computer Vision, Image Processing, Medical and Health Sciences, Signal Processing
- Bachelor Thesis, Master Thesis, Semester Project