Zurich HeartOpen OpportunitiesIn this project, we want to explore possible extensions of predictive control barrier functions to the multi-agent setting. Predictive control barrier functions [1] allow certifying safety of a system in terms of constraint satisfaction and provide stability guarantees with respect to the set of safe states in case of initial feasibility. This allows augmenting any human or learning-based controller with closed-loop guarantees through a so-called safety filter [2] which is agnostic to the primary control objective. As current formulations are restricted to single agents, the goal is to investigate how this formulation can be extended for multi-agent applications and how the interactions between the agents can be exploited in order to reduce computational overhead. - Engineering and Technology, Systems Theory and Control
- Master Thesis
| This project focuses on developing autonomous robots for synchronized performances on water. Equipped with kinetic water fountains, RGB lighting, and ultrasonic mist generators, the robots are designed to execute planned choreographies. The system utilizes robotics control, wireless communication, and positioning technologies to coordinate movements, and payload activation, facilitating complex pattern generation and synchronization. The objective is to advance the application of distributed robotic systems in creating structured and cohesive visual displays on water. - Arts, Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| Geothermal energy will be one of the most important assets to solve the world’s energy problems in the future. High Speed Rock Drilling (HSRD), a Swiss company, has been developing and testing an efficient process for deep drilling down to 10 km. - Mechanical and Industrial Engineering
- Master Thesis
| If you wear glasses, you know exactly how cumber-some it can be when your glasses fog up.
In our startup Solabs Nanotechnology, we investigate a lot of fundamental and applied phenomena to inhibit fogging. In this specific project, we employ a trans-parent, nanoscopically thin coating to prevent or re-move fog efficiently solely based on sunlight. Our coating specifically absorbs near-infrared radiation, which is not visible to the human’s eye, while it retains transparency in the visible spectrum. The absorbed energy heats up the surface and prevents fog for-mation. A global patent application for our technology is pending.
- Environmental Engineering, Materials Engineering, Mechanical and Industrial Engineering, Optical Physics
- ETH Zurich (ETHZ), Master Thesis
| Diaxxo (diaxxo.com) is a start-up originated at ETH Zürich, where in the past 4 years we have
developed and prototyped an innovative Point-of-Care Polymerase Chain Reaction (PCR) device. Our
vision is to bring the power of molecular diagnostics to every doctor’s office, thanks to our new
pre-loaded PCR test kits that allow for simpler and faster sample preparation steps. In 2020 our
devices have been used by more than 250 laypeople for the rapid detection of COVID-19 in a field
study that we run at ETH Zürich, and our systems are currently being tested at the Swiss Tropical and
Public Health Institute (Swiss TPH). - Biology
- Bachelor Thesis, Master Thesis, Semester Project
| Diaxxo (diaxxo.com) is a start-up originated at ETH Zürich, where in the past 4 years we have
developed and prototyped an innovative Point-of-Care Polymerase Chain Reaction (PCR) device. Our
vision is to bring the power of molecular diagnostics to every doctor’s office, thanks to our new
pre-loaded PCR test kits that allow for simpler and faster sample preparation steps. In 2020 our
devices have been used by more than 250 laypeople for the rapid detection of COVID-19 in a field
study that we run at ETH Zürich, and our systems are currently being tested at the Swiss Tropical and
Public Health Institute (Swiss TPH). - Engineering and Technology
- Master Thesis, Semester Project, Student Assistant / HiWi
| Studying the long-term diffusion of solutes in metals is crucial for a variety of present and futuristic engineering applications. This includes the design of safe and compact solid-state hydrogen reservoirs for automobile applications, designing corrosion-resistant materials for nuclear applications, and much more. The time scales involved in such mass diffusion processes for potential applications range from seconds to minutes. However, most state-of-the-art atomistic techniques can simulate an ensemble of atoms as large as some micrometers and for a real-time of some microseconds at best. Hence, the computational modeling of atomistic mass diffusion presents many challenges, which is why the design of these devices has relied on experiments. This project deals with an emerging class of atomistic simulation techniques based on statistical mechanics, which aims to track the relevant statistics of the ensemble rather than tracking all atomic positions and momenta. In such a statistical framework with multiple atomic species, every atomic site ceases to be a pure species and is instead identified by probabilities of finding different types of species at that site.
In order to introduce mass transport in such a setting, one needs to update the concentrations of different species at the atomic sites based on a phenomenological model, or by an atomistically informed master equation for the site probabilites. We are more interested in the latter approach, which involves computing the energy barriers and minimum energy pathways needed for atoms of different types to hop from one site to another. As this computation needs to be done for every possible atomic hop in the ensemble, the concentration update becomes computationally expensive. In this project, we plan to bypass this by employing graph neural networks (GNNs) to learn the hopping energy barriers as a function of local atomic environments and using a pre-trained GNN to update the site probabilities, which would enable us to reach higher time scales relevant for potential applications. - Engineering and Technology
- Master Thesis
| Anaerobic digestion (AD) is considered one of the oldest and most sustainable biological treatment technologies for stabilizing and reducing organic waste, including food waste, sewage sludge, industrial waste, and farm waste. AD transforms organic matter into biogas (60–70 vol-% of methane), thereby reducing the volume of the waste whilst destroying some of the pathogens present in the waste feedstocks and limiting odor problems associated with waste materials (Appels et al., 2008; Gerardi, 2003). AD is a promising energy, waste management, and sanitation solution in low-resource, low-income settings (Forbis-Stokes et al., 2016; Owamah et al., 2014). However, it does not fully eliminate pathogens for safe environmental discharge. Three ETH master students (Hardeman, 2022; Jäggi, 2023; Luz, 2022) iteratively developed and optimized the biogas reactor and the solution for sludge pasteurization to homogeneously heat the effluent and render the liquid safe for discharge. However, the technology needs further improvements and adaptations to operate reliably in continuous mode in all environmental conditions. - Mechanical Engineering
- ETH for Development (ETH4D) (ETHZ), ETH Zurich (ETHZ), Master Thesis
| Acoustic Droplet Vaporization is the phase-change process of superheated droplets (usually micrometric or nanometric in size) triggered by the exposure to an ultrasound wave. This phenomenon can be greatly exploited in the biomedical field for application like drug delivery and embolotherapy. Ultrasound imaging is an effective way to study and characterize it, with the final goal to improve the safety and efficacy of this treatment. - Biomechanical Engineering, Fluidization and Fluid Mechanics
- ETH Zurich (ETHZ), Semester Project
| Unlike crystalline materials which are characterized by well ordered atomic arrangements of a clear repeated unit-cell, amorphous solids cannot be described by a tessellated representative volume element due to the disorder of their microstructures. One example of amorphous solids are metallic glasses that typically have high resistance to plastic deformations thanks to the absence of grain boundaries, yet their failure modes are rapid and catastrophic. Recently it was shown that a macroscale analogy can be achieved with truss metamaterials, as introduction of disorder can lead to similar characteristics to those exhibited by amorphous solids.
On our ongoing research we study reconfigurable truss metamaterials, whose members are modelled as "bendy-straws" which are characterized by local multistability. Namely, in 2D each constituent segment of every straw has four stable equilibria, which provide a straw-based truss metamaterial a myriad of multiaxial stable configurations. Thus, a careful design can lead to structures with different operative stable states, whose reconfigurations do not involve introducing plastic deformations. So far, we mainly focused on periodic straw-based structures, yet in inspiration of amorphous solids and the interesting behavior they introduce, we wish to expand this study to disordered arrangements. Such structures are expected to provide high resilience and damage tolerance. Moreover, since large deformations are manifested in post buckling rather than plastic deformations, instabilities such as shear bands which cause irreversible plastic deformations in metallic glasses and truss metamaterials, can in this case be leveraged for rapid reconfiguration. Thanks to this characteristic, studying disorder in reconfigurable structures lays the foundations for nondestructive experimentation of plastic phenomena. - Mechanical Engineering
- ETH Zurich (ETHZ), Master Thesis
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