**D-MAVT** **D-MAVT** Profit from a great search interface and directly apply to the position of your choice. SiROP - Excellence in Science! Profit from a great search interface and directly apply to the position of your choice. SiROP - Excellence in Science! Open projects at the Department of Mechanical and Process Engineering ETH Open projects at the Department of Mechanical and Process Engineering ETH Iron deficiency anemia (IDA) is one of the most widespread nutritional deficiencies worldwide, increasing the risk for disability and death for more than two billion people. Iron supplements are needed for prevention of iron deficiency, especially among infants, children and pregnant women, and for correction of IDA in all affected individuals. Conventional iron supplements, commonly cause nausea, epigastric discomfort and other gastrointestinal side effects that lead many individuals to discontinue and avoid their use.
In this project, gastric resident systems (GDSs) will be produced using advanced manufacturing approaches (e.g., 3D printing) and the resulting release kinetic of the bioactive compounds will be characterized. Based on the results, different GDSs 3D design, formulations, and combination of active compounds will be tested. - Biology, Chemistry, Engineering and Technology, Medical and Health Sciences
- Master Thesis, Semester Project
| In this project, we want to explore the application of predictive stability filters for automotive applications. Predictive stability filters allow augmenting human or learning-based controllers such that safety in terms of constraint satisfaction as well as stability of a desired setpoint can be guaranteed. Such algorithms present possible solutions for automotive applications such as, e.g., lane keeping. - Engineering and Technology, Systems Theory and Control
- Master Thesis
| The primary objective of this project is to develop an automated pipeline for the identification and recognition of patterns within urodynamic recordings, utilizing urodynamic recording data in conjunction with annotated patterns provided by experts. This endeavor seeks to reduce the susceptibility of interpreting urodynamic recordings to potential errors arising from human judgment and inaccuracies, thereby improving the management of urinary tract complications in patients with spinal cord injury. By implementing a systematic approach to pattern recognition in Bladder Valomue/Pressure Time Series Measurements of urodynamic data, the potential for error in decision-making can be significantly reduced. - Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Biosensor Technologies, Computer Hardware, Computer-Human Interaction, Electrical and Electronic Engineering, Engineering/Technology Instrumentation, Mechanical Engineering, Medical Biotechnology
- Internship, Master Thesis, Semester Project
| Bühler, a leading industry manufacturer in Uzwil, is partnering with ETH Zürich's Feasibility Lab to offer a unique master thesis opportunity. Throughout your thesis, you'll work hand-in-hand with a team of like-minded peers, following the principles of cross-functional teamwork and agile project planning. You can explore your interests in AI/Machine Learning, Robotics, UX, Additive Manufacturing, Food Science and more and actively define your own project scope. - Digital Systems, Environmental Technologies, Industrial Biotechnology and Food Sciences, Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Drying (e.g. Pasta drying) is the most energy intensive process step, sometimes taking up more than 50% of the total energy consumption of a plant. Superheated steam drying could present an energy efficient alternative to classical hot-air drying systems used today. This new technology could have a massive impact on the carbon-footprint and sustainability of food-drying; making it a highly future-oriented and potentially impactful innovation. - Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Drying (e.g. Pasta drying) is the most energy intensive process step, sometimes taking up more than 50% of the total energy consumption of a plant. Superheated steam drying could present an energy efficient alternative to classical hot-air drying systems used today. This new technology could have a massive impact on the carbon-footprint and sustainability of food-drying; making it a highly future-oriented and potentially impactful innovation. - Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| The goal of the project is to develop and test a smart sock prototype for plantar pressure measurements. The smart sock contains textile based pressure sensors and a readout module. This technology can be used for plantar pressure monitoring in diverse wearable applications ranging from healthcare to sports. - Biomedical Engineering, Medical and Health Sciences
- Master Thesis
| The efficient operation of excavators in construction environments necessitates precise pose estimation of their buckets. Current methods rely on IMUs placed on the excavator arm which require tedious calibration and can be damaged during construction operations. This project aims to leverage computer vision and machine learning to enhance pose estimation, thereby enabling VR overlays for teleoperation and facilitating automation tasks. - Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| We are developing a teleoperated micro-assembly system. A core component is a force-sensitive micro-gripper. A first gripper prototype has been realized and evaluated. Your task will be to review and improve the current design and to implement automated object slippage detection. - Mechanical and Industrial Engineering, Robotics and Mechatronics
- Master Thesis
| In many autonomous navigation applications, the robot must interact with the environment to learn and complete tasks. Furthermore, these applications are safety-critical, and crashes cannot be afforded. This necessitates the safe learning of the unknown environment in order to achieve the task objective (e.g., detecting a leak or mapping an area). For example, consider an application of safe exploration in a warehouse with a wheeled robot to identify the source of a gas leak. - Mechanical Engineering
- Master Thesis
| Join a team of scientists improving the long-term prognosis and treatment of Spinal Cord Injury (SCI) through mobile and wearable systems and personalized health monitoring.
Joining the SCAI Lab part of the Sensory-Motor Systems Lab at ETH, you will have the unique opportunity of working at one of the largest and most prestigious health providers in Switzerland: Swiss Paraplegic Center (SPZ) in Nottwil (LU). - Artificial Intelligence and Signal and Image Processing, Computer Software, Data Format, Information Systems
- ETH Zurich (ETHZ), Internship, Lab Practice, Student Assistant / HiWi
| A key barrier hindering the swift introduction of autonomous vehicles (AVs) in real-world contexts is the challenge in establishing clear safety benchmarks. Specifically, the issue of systematically assessing both performance and safety remains a significant stumbling block within the industry.
This challenge is mainly twofold: Firstly, how can we identify an ideal scenario set to evaluate the vehicle's performance within a targeted Operational Design Domain (ODD) and what criteria would be useful in amplifying or paring down this set?
Secondly, how do we determine a substantial stopping criteria for the evaluation campaign, and what level of confidence should be attached to the observed performances? - Applied Statistics, Automotive Engineering, Intelligent Robotics, Other
- Master Thesis, Semester Project
| Meeting the demands of evolving manufacturing and environmental landscapes frequently entails the development of pioneering processes and materials. Yet, generating innovative materials presents its own set of challenges. This project necessitates the establishment of a methodology for producing rods and wires, facilitating the production of powder for novel alloys. To accomplish this task, a forging machine known as a round swage will be employed. The created rods and wires will be used to produce powder using Ultrasonic Plasma Atomizer (UPA) and the wires will be used as is for Directed Energy Deposition (DED), an additive manufacturing technology. - Alloy Materials, Composite Materials, Manufacturing Engineering
- ETH Zurich (ETHZ), Master Thesis
| see attachment - Engineering and Technology
- Other specific labels
| The stochastic diffusion equations ruling the dynamics of particles at the micro- and nano- scale are captured by energy-minimizing dynamics when observed macroscopically, i.e., at a population level. This framework encompasses, for instance, single cells perturbation responses to chemical, genetic or mechanical stimuli, gene expression and cell differentiation.
Recent advances in the theory of optimal transport and optimization in the Wasserstein space have created unprecedented opportunities to tackle these and other problems at scale. This active research area provides an excellent playground for exploring advanced mathematical concepts, deploying sophisticated learning and optimization algorithms, and solving open problems in biology, medicine, and various other fields.
The project can be both theoretical and applied, and can include topics on optimization, optimal transport, deep learning, and biology. The project can be tailored to the preferences and experiences of the student. - Artificial Intelligence and Signal and Image Processing, Biomaterials, Calculus of Variations and Control Theory, Optimisation, Physical Chemistry
- Master Thesis, Semester Project
| Wind turbines operate in the first few hundred metres above ground level. In this area, the wind is turbulent and gusty, generating unsteady aerodynamic forces that cause premature structural damage and reduced performance. A better understanding of unsteady turbulent flows over aerofoils would help to improve the estimation of the dynamic forces.
The MISTERY project involves an interdisciplinary team of researchers in aerodynamics, machine learning and electronics, from ETHZ and OST in Switzerland, and CentraleSupélec and Centrale Nantes in France. The team investigates the impact of turbulence on aerodynamic performance of wind turbine blades. To achieve this, we study the aerodynamics of a 1:1 scale of a section of a wind turbine blade in a large wind tunnel (4m x 5m with wind speeds up to 50m/s) in Nantes (figure 1). The blade is instrumented with over 300 pressure sensors and the flow is visualised with PIV. These measurements will create a large open database that will be used to develop models for flow control of for structural health monitoring.
- Aerodynamics, Mechanical Engineering
- Collaboration, ETH Zurich (ETHZ), Master Thesis
| This project focuses on developing robust reinforcement learning controllers for agile drone navigation using adaptive curricula. Commonly, these controllers are trained with a static, pre-defined curriculum. The goal is to develop a dynamic, adaptive curriculum that evolves online based on the agents' performance to increase the robustness of the controllers. - Engineering and Technology
- Master Thesis, Semester Project
| Adherence to rehabilitation therapy is crucial for the recovery of hand functionality in stroke and traumatic brain injury (TBI) patients. However, sustaining patient motivation to train at home remains a challenge. This project aims to explore the impact of push notifications on adherence to physical therapy among stroke and TBI patients. By investigating the optimal frequency and content of notifications, the goal is to develop a notification/reminder system that fosters continuous engagement with the rehabilitation plan, ultimately promoting increased therapy and better functional outcomes for patients. - Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Internship, Master Thesis, Student Assistant / HiWi, Summer School
| Improving and characterizing hardware system to experimentally investigate the interactions of tachycardia and ration therapy. - Biomedical Engineering
- Semester Project
| Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment.
Your work will guide future rehabilitation methods in general clinical practice, through applied classification and dimensionality reduction in Biomechanics of walking.
Goal: Develop an unsupervised clustering pipeline for a large dataset of gait patterns from spinal cord injured individuals for class similarity evaluation
- Engineering and Technology, Expert Systems, Medical and Health Sciences, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling
- Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis
| This project addresses the task of 6D pose estimation for general-purpose objects, particularly when dealing with occlusion. We aim to leverage recent deep learning methods and synthetic data generation schemes to enable robust object manipulation. - Intelligent Robotics
- Master Thesis, Semester Project
| In the past few years, there has been significant progress in developing 3D in vitro cancer models. These models serve as a link between 2D cell culture models and in vivo xenograft mouse models, which are considered the gold standard in cancer research and preclinical drug assessment. Various 3D methods have been explored, and among them, spheroids have shown great potential as an alternative to traditional methods. These are often used in a scaffold-free context lacking the physical environment and interactions present in vivo. Therefore, scaffold-based approaches have gained more attention due to their ability to mimic the tumor microenvironment (TME), which is a crucial factor in tumor behavior. By providing a scaffold that mimics the TME, we can gain a better understanding of the influence of TME on tumor spheroid behavior and drug response.
This project aims to establish a 3D scaffold-based spheroid tumor model that mimics the behavior of human squamous cell carcinoma (SCC) at varying degrees of aggressiveness. The model will be designed to replicate the tumor and its microenvironment using a molecular and biophysical defined system. The ultimate objective is to create optimized models that have a physiological similarity to human SCC, which can enhance overall knowledge and increase the predictive value, enabling preclinical-to-clinical translation. By doing this, we hope to provide a 3D in vitro model that can reduce and potentially replace the use of animal models as whenever possible. - Biology, Biomedical Engineering, Medical and Health Sciences
- Internship, Master Thesis
| This thesis aims to utilize deep learning techniques to analyze eye-tracking data during a goal-directed upper limb task, particularly focusing on participants under the influence of alcohol. The objective is to develop digital health metrics that can elucidate differences in movement planning. - Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| To limit climate change and global warming to below 2°C; substantial emission reductions will be needed
to reach net-zero anthropogenic CO2 emissions by 2050 at the latest. Carbon capture and storage (CCS)
will be a key instrument for mitigating hard-to-abate point-source emissions. Another environmental
challenge of this century is the large amounts of waste materials produced by industry, which are often
landfilled or used for low-value applications and have a detrimental environmental impact due to the
leaching of heavy metals. For industrial waste management and small-scale CCS, a solution is offered
by ex-situ mineral carbonation: an accelerated form of natural rock weathering, i.e., the formation of
stable carbonates by the reaction of CO2 with naturally occurring oxides or silicates of magnesium, iron,
and calcium. Many industrial residues have been studied for mineralization, including iron and steel-making
slags, fuel combustion ashes, mine tailings, alkaline paper mill wastes, cement kiln dusts, and recycled
concrete aggregates. Mineralization of these industrial residues has the potential to permanently
store up to 360 Mt of CO2 per year in the form of carbonated minerals and generates value through the
use of the resulting products. One mineralisation pathway involves using a solvent, aqueous ammonium salts,
to accelerate the process. However, for economical and environmental reasons, the solvent must be recycled
multiple times. This aspect is often forgotten or not investigated in the literature. A novel experimental setup has
been developed to perform multiple cycle experiments, and this setup will be used to assess the process
stability and performance over multiple recycling cycles of the solvent. - Environmental Technologies, Mechanical Engineering, Membrane and Separation Technologies
- ETH Zurich (ETHZ), Master Thesis
| See attached pdf - Economics, Engineering and Technology, Mathematical Sciences, Policy and Political Science
- Bachelor Thesis, Master Thesis
| This project focuses on utilizing various techniques for Video to Events generation. - Computer Vision
- Master Thesis
| In 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
| The goal of the project is to assess the feasibility of using commercially available plantar pressure monitoring devices (so called smart insoles) on the diabetic population. Pressure ulcers are a common complication of the diabetic foot, and monitoring plantar pressure continuously is a potential measure of prevention. Diabetic patients are often prescribed personalized footwear (e.g., curved insoles that accommodate any deformity in the feet). This project aims at assessing the potential of the smart insoles available on the market to monitor plantar pressure in diabetic patients with such custom footwear. - Biomedical Engineering, Medical and Health Sciences
- Bachelor Thesis, Semester Project
| The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
- Information, Computing and Communication Sciences
- Master Thesis
| Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
- Information, Computing and Communication Sciences
- Master Thesis
| In recent years, advancements in reinforcement learning have achieved remarkable success in teaching robots discrete motor skills. However, this process often involves intricate reward structuring and extensive hyperparameter adjustments for each new skill, making it a time-consuming and complex endeavor. This project proposes the development of a skill generator operating within a continuous latent space. This innovative approach contrasts with the discrete skill learning methods currently prevalent in the field. By leveraging a continuous latent space, the skill generator aims to produce a diverse range of skills without the need for individualized reward designs and hyperparameter configurations for each skill. This method not only simplifies the skill generation process but also promises to enhance the adaptability and efficiency of skill learning in robotics. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| Recent advances in physically simulated humanoids have broadened their application spectrum, including animation, gaming, augmented and virtual reality (AR/VR), and robotics, showcasing significant enhancements in both performance and practicality. With the advent of motion capture (MoCap) technology and reinforcement learning (RL) techniques, these simulated humanoids are capable of replicating extensive human motion datasets, executing complex animations, and following intricate motion patterns using minimal sensor input. Nevertheless, generating such detailed and naturalistic motions requires meticulous motion data curation and the development of new physics-based policies from the ground up—a process that is not only labor-intensive but also fraught with challenges related to reward system design, dataset curation, and the learning algorithm, which can result in unnatural motions.
To circumvent these challenges, researchers have explored the use of latent spaces or skill embeddings derived from pre-trained motion controllers, facilitating their application in hierarchical RL frameworks. This method involves training a low-level policy to generate a representation space from tasks like motion imitation or adversarial learning, which a high-level policy can then navigate to produce latent codes that represent specific motor actions. This approach promotes the reuse of learned motor skills and efficient action space sampling. However, the effectiveness of this strategy is often limited by the scope of the latent space, which is traditionally based on specialized and relatively narrow motion datasets, thus limiting the range of achievable behaviors.
An alternative strategy involves employing a low-level controller as a motion imitator, using full-body kinematic motions as high-level control signals. This method is particularly prevalent in motion tracking applications, where supervised learning techniques are applied to paired input data, such as video and kinematic data. For generative tasks without paired data, RL becomes necessary, although kinematic motion presents challenges as a sampling space due to its high dimensionality and the absence of physical constraints. This necessitates the use of kinematic motion latent spaces for generative tasks and highlights the limitations of using purely kinematic signals for tasks requiring interaction with the environment or other agents, where understanding of interaction dynamics is crucial.
We would like to extend the idea of creating a low-level controller as a motion imitator to full-body motions from real-time expressive kinematic targets. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| The Advanced Manufacturing Lab (am|z) is excited to announce a thesis opportunity focusing on the development of a highly parallelizable modeling framework for additive manufacturing (AM) processes, particularly laser powder bed fusion (LPBF). Our research primarily delves into advancing manufacturing techniques, with a special emphasis on additive manufacturing. We have developed a robust numerical simulation framework called iMFREE utilizing Smoothed Particle Hydrodynamics (SPH) for multi-physics applications like LPBF. However, there is a need to enhance computational efficiency, specifically through parallelization via Message Passing Interface (MPI). This project offers an excellent chance for students to deepen their knowledge in parallel computation while working hands-on with a mature computational framework. - Engineering and Technology, Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), Master Thesis
| In the burgeoning field of deep reinforcement learning (RL), agents autonomously develop complex behaviors through a process of trial and error. Yet, the application of RL across various domains faces notable hurdles, particularly in devising appropriate reward functions. Traditional approaches often resort to sparse rewards for simplicity, though these prove inadequate for training efficient agents. Consequently, real-world applications may necessitate elaborate setups, such as employing accelerometers for door interaction detection, thermal imaging for action recognition, or motion capture systems for precise object tracking. Despite these advanced solutions, crafting an ideal reward function remains challenging due to the propensity of RL algorithms to exploit the reward system in unforeseen ways. Agents might fulfill objectives in unexpected manners, highlighting the complexity of encoding desired behaviors, like adherence to social norms, into a reward function.
An alternative strategy, imitation learning, circumvents the intricacies of reward engineering by having the agent learn through the emulation of expert behavior. However, acquiring a sufficient number of high-quality demonstrations for this purpose is often impractically costly. Humans, in contrast, learn with remarkable autonomy, benefiting from intermittent guidance from educators who provide tailored feedback based on the learner's progress. This interactive learning model holds promise for artificial agents, offering a customized learning trajectory that mitigates reward exploitation without extensive reward function engineering. The challenge lies in ensuring the feedback process is both manageable for humans and rich enough to be effective. Despite its potential, the implementation of human-in-the-loop (HiL) RL remains limited in practice. Our research endeavors to significantly lessen the human labor involved in HiL learning, leveraging both unsupervised pre-training and preference-based learning to enhance agent development with minimal human intervention. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| The study of small-molecule supramolecular hydrogelators (SMSHs) is of great interest, both fundamental and applicative. Their self-assembly most often leads to the formation of fibrillar structure and can be used as a model for the fibrillation of biologically-relevant entities, also their ability to form gels with tunable mechanical properties suggest many promising materials-related applications. In this context, aminoacid-based SMSHs (AA-SMSHs) have a special relevance because of opportunities offered e.g. in terms of biocompatibility and biomimetics, as well as in terms of variety of molecular design possibilities. Usually, the sol-gel behavior of AA-SMSHs is pH-dependent thanks to the presence of one or more pH-responsive groups, especially carboxylic acid –COOH ones. For these reasons, pH-responsive SMSHs (aminoacid-based and non) have been and still are the subject of intense investigation. Nevertheless, their behavior is far from being completely understood. - Biological and Medical Chemistry, Biomaterials, Materials Engineering, Physical Chemistry of Macromolecules, Supramolecular Chemistry
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| Are you interested in designing novel hydrogel materials? We have a project available that focuses on formulating high-performance hydrogels for load-bearing applications. - Chemistry, Engineering and Technology
- Bachelor Thesis, Master Thesis, Semester Project
| Cartilage damage in the knee joint can be caused by aging or repetitive actions. It can be treated by surgically removing the damaged cartilage tissue and filling the generated defect with a precisely shaped, healthy cartilage graft. Removing the defected cartilage is commonly done with surgical curettes. We are investigating the use of laser ablation for a more precise defect preparation process. With two different lasers, we managed to obain promising results regarding cell viability in live samples. However, laser parameters such as pulse frequency and energy need to be optimized towards higher cutting efficiency. Your task will be to prepare a setup to test, optimize, and validate various parameter sets using different lasers for articular cartilage ablation. - Biomedical Engineering, Optical Physics
- Master Thesis
| gpuFlightmare: High-Performance GPU-Based Physics Simulation and Image Rendering for Flying Robots - Engineering and Technology
- Master Thesis, Semester Project
| Fly like a bird - Engineering and Technology
- Master Thesis, Semester Project
| Visual Representation Learning for Efficient Deep Reinforcement Learning
- Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Despite the growing amount of work on applying causal discovery method with expert knowledge to areas of interest, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since that in scientific fields, causal discovery with expert knowledge should be cautious and an approach taking expert uncertainty into account will be more robust to potential bias induced by individuals. Therefore, we aim to develop an iterative causal discovery method with experts in the loop to enable continual interaction and calibration between experts and data.
Based on the qualifications of the candidates, we can arrange a subsidy/allowance for covering traveling or living costs. - Expert Systems, Health Information Systems (incl. Surveillance), Statistics
- Internship, Master Thesis, Semester Project
| The premise Python library is a comprehensive tool designed for the integration and analysis of emerging tech-nologies (e.g., battery electric vehicles, synthetic fuels) using “futurized” life-cycle inventories (LCIs). The library is now used by hundreds of researchers. As part of our commitment to accuracy, transparency, and usability, we are seeking a Master's student with a passion for sustainability, environmental science, or a related field, to assist in the enhancement of our documentation, the refinement of life-cycle inventory descriptions, and the rig-orous quality assurance of our datasets. This internship presents an opportunity to contribute to a vital resource used by researchers and professionals worldwide to make informed decisions on sustainability - Engineering and Technology
- Internship
| In recent years, using deep Reinforcement Learning (RL) for robotic motion policies has demonstrated impressive performance, yielding unprecedented robustness on real hardware. Current sim2real approaches rely on large-scale pre-training with domain randomization to make policies robust but struggle with high-dimensional spaces. Current RL methods are primarily limited by their low sample efficiency. Leveraging differentiable simulators for first-order gradient information shows great results for enhancing sample efficiency. Although promising simulation results exist, deployment on hardware is not usually done. The goal of this thesis is to train quadrupedal locomotion policies in a differentiable simulation framework, and then enable real-world deployment by modifying the simulation, the policy training, or the learning algorithm. Ideally, we can leverage properties of differentiable simulators in this process to improve sim2real transfer by fitting real data. - Intelligent Robotics, Robotics and Mechatronics
- 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
| Understanding the distribution and mechanics of velocity and pressure within microaneurysms is crucial for controlling microrobots navigating through them. Traditional methods for velocity and pressure measurement in microchannels, such as particle image velocimetry (PIV) and numerical simulations based on fluidic physics laws, suffer from high computational demands and inability to operate in real-time. Moreover, pure image methods struggle with near-wall regions lacking visible particles. Leveraging recent advancements in machine learning, particularly convolutional neural networks (CNNs), this project proposes a novel approach - a physics-informed CNN integrated with Navier-Stokes equations and optical flow equations. This CNN aims to accurately predict velocity and pressure profiles in microchannel flows in real-time using only flow images and essential physical parameters. The network architecture comprises an encoder-decoder structure with seven convolutional layers, incorporating down-sampling and up-sampling layers. The final output layer produces three channels representing horizontal velocity, vertical velocity, and pressure. Additionally, a physics-informed loss function, incorporating dimensionless Navier-Stokes equation residuals and optical flow equation residuals, enhances the model's performance by integrating knowledge of fluid dynamics and computer vision. This approach represents a promising advancement towards achieving real-time, high-accuracy prediction of velocity and pressure fields in microchannel flows, with potential applications in microrobotics and microfluidics. - Computer Vision, Engineering and Technology, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Physics
- Bachelor Thesis, Master Thesis, Semester Project
| 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
| This thesis focuses on fully automating the evaluation of Raman spectra in a self-driven thermodynamics lab to accelerate the development of sustainable chemical processes or novel heat pump concepts. By integrating Machine Learning (ML) with advanced spectral evaluation algorithms, the aim is to achieve complete lab autonomy. The methodology combines data-driven and physically-based approaches, including synthetic spectrum generation for ML training. - Biomedical Engineering, Chemical Engineering, Mechanical and Industrial Engineering, Physical Chemistry, Physics
- Master Thesis
| This project will be based on the preliminary results obtained from a previous master project in causal graphical modeling of autonomous dysreflexia (AD). The focus of the extension would be two-fold. One is improving the temporal graphical reconstruction for understanding the mechanism of AD. The other one is building a forecasting framework for the early detection and prevention of AD using the graph structure we constructed before. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided. - Artificial Intelligence and Signal and Image Processing, Autonomic Nervous System
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| This project focuses on developing an explainable Artificial Intelligence (xAI) framework based on graphical modeling (GM), to enhance the capacity and capability of medical AI. Collaborating with the Swiss Paraplegic Centre (SPZ) for validation, our goal is to improve the long-term prognosis of spinal cord injury (SCI) individuals. Through medical records and a multimodal sensory monitoring system, we aim to create digital twins capable of integrating diverse data sources, guiding medical treatment, and addressing common secondary health conditions in the SCI population. The envisioned GM-based digital twin (GMDT) will represent hierarchical relations across demographic features, functional abilities, daily activities, and health conditions for SCI individuals, allowing for downstream tasks such as prediction, causal inference, and counterfactual reasoning. The assimilation and evolution between the physical and digital twins will be implemented under the GM framework, promising advancements in personalized healthcare strategies and improved outcomes for SCI people. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided. - Biomedical Engineering, Digital Systems, Knowledge Representation and Machine Learning, Pattern Recognition, Simulation and Modelling
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| Embark on a journey with the Swiss watch industry, renowned for its dedication to handcrafted excellence. Together, we're delving into the realm of advanced materials to enhance the art of watchmaking. Our focus lies in developing a groundbreaking photo-cleavable crosslinker, a key player in the application of resins onto watch dials as temporary masks during surface finishing. Join us in pioneering the fusion of craftsmanship and cutting-edge technology! - Organic Chemical Synthesis, Polymers
- ETH Zurich (ETHZ), Master Thesis
| We want to expand the use of acoustic microrobots for biomedical applications by studying their manipulation in 3D environments and their simultaneous real time tracking using non-invasive ultrasound imaging. - Biomedical Engineering, Materials Engineering, Mechanical and Industrial Engineering
- Bachelor Thesis, Master Thesis, Semester Project, Summer School
| We want to expand the use of acoustic microrobots for drug delivery biomedical applications in brain tumor environments of small mammalian models. - Biology, Biomedical Engineering, Materials Engineering, Mechanical and Industrial Engineering
- Master Thesis
| We want to expand the use of acoustic microrobots for drug delivery applications in tumor environments. - Biomedical Engineering, Electrical and Electronic Engineering, Industrial Biotechnology and Food Sciences, Materials Engineering, Mechanical and Industrial Engineering
- Bachelor Thesis, Collaboration, Master Thesis, Semester Project
| The goal of the project is to synthesize and characterize a number of small molecules capable of acting as mechanophore addition to various polymers. These polymers would then be used as wearable strain or pressure sensors. - Chemical Engineering, Chemistry, Composite Materials
- Master Thesis
| Photo-reversible chemistries have opened new possibilities especially in the field of biomedical engineering and our lab has contributed to this process by research on hydrogels based on various dynamic chemistries. We now want to adapt one known and working photo-cleavable linker in a system that is based on organic solvents rather than water. This would allow for the use of the material in a wide range of industrial applications including the digital printing of temporary masks during surface treatments. - Macromolecular Chemistry, Mechanical Engineering, Polymers, Printing Technology
- Master Thesis, Semester Project
| Recent work on multi-robot systems with collaborative autonomy has made significant strides towards developing robotic
teams capable of performing complex tasks in real, complex settings as shown above. Right at the core of such capabilities is
the capability to collaboratively perform SLAM (Simultaneous Localization And Mapping) within such multi-agent systems that
can operate efficiently and in challenging real-world environments, which is the main goal of this project.
The aim of this project is to develop key components of a multi-robot SLAM system that is robust in challenging environments
and adaptable to different scenarios, ranging from environmental monitoring to search-and-rescue operations. The envisioned
system will research integrating complementary onboard sensor modalities (e.g., cameras, LiDAR, and IMU), machine learning
methods, and distributed communication systems to provide precise localization and mapping exhibiting resilience to sensor
failure and sufficient efficiency to be deployed onboard small platforms, such as drones. The student will be guided to work
towards a system architecture that can enable effective testing and optimization in state-of-the-art simulation engines, with the
ultimate goal of reducing the gap between simulated experiments and real tests. The outlook is to create a system that can be
employed onboard a small swarm of drones in a real setting. - Computer Vision, Intelligent Robotics
- Master Thesis
| Automating drone navigation promises to revolutionise the way we conduct a wide variety of tasks, such as agricultural monitoring, industrial inspection, and disaster relief scenarios. Equipping a drone with the capability to autonomously explore and map previously unseen environments using onboard sensors and algorithms forms the basis of autonomy. While there has been tremendous progress in this area over the past few years [1-5], existing systems still lack reliability and adaptability to the challenges and complexity of real settings, which is crucial for the deployment of this technology in actual missions. In particular, performing robust navigation and mapping in highly dynamic environments (e.g., forests) remains an open challenge.
Following promising leads from the state-of-the-art and our in-house navigation stack, the goal of this project is to develop the capability to deal with increasingly dynamic and complex scenarios. The student will be guided towards leveraging the multi-sensor capabilities of a LiDAR-Visual-Inertial payload being developed in the lab to research approaches for perception and mission planning that can fuse information from the different sensors and capture high-fidelity representations of challenging dynamic environments. Initially, the student will work within a realistic simulation environment and then deploy and test their work onboard a real drone in a real setting.
- Computer Vision, Intelligent Robotics
- Master Thesis
| The collaboration between Advanced Manufacturing Lab (am|z) and Automatic Control Lab (IFA) is centered on developing a novel scan path generator for a laser powder bed fusion (PBF) machine capable of processing multiple materials simultaneously. The aim is to integrate the Machine Control Framework (AMCF) with our machine control system to enhance controlability and reliability. - Electrical and Electronic Engineering, Mechanical Engineering, Programming Techniques
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis
| Navigating the unpredictable off-road environment, autonomous robots require a tailored approach to overcome obstacles and optimize pathfinding. Our proposed terrain cost mapping system goes beyond traditional processing by factoring in each robot's specific kinematic abilities. We introduce a novel simulation-based Roll-Out technique to predict a robot's stability over varied terrains, thereby calculating a precise terrain cost. This innovative strategy promises to enhance autonomous navigation by ensuring safe and efficient traversal tailored to individual robotic capabilities. - Intelligent Robotics
- Master Thesis, Semester Project
| This master thesis project centers on the development of a foundation model for drone navigation within confined spaces such as ballast tanks of ships. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| Develop an end-to-end learning-based approach for autonomous drone navigation in ship ballast tank manholes, incorporating both real and simulated training data. The project aims to emphasize speed, a high success rate, and safety in flying through the confined spaces of ship interiors. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| This project focuses on using RL to learn quadrotor policies to fly at high speeds in complex tracks, directly from features.
- Intelligent Robotics, Robotics and Mechatronics
- Master Thesis
| This master thesis project focuses on advancing 3D scene understanding through the integration of segmentation and object detection techniques within Neural Radiance Fields (NeRFs). - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| This project is focused on developing a vision-only flight recovery system for autonomous drones. A critical capability for autonomous drones is to recover safely from any unstable state. This project explores the potential of using reinforcement learning to enable a drone to transition from an unstable to a stable state, using only vision sensors. The challenge lies in creating a system that not only stabilizes the drone but also ensures it can safely land in various unforeseen scenarios.
- Intelligent Robotics, Robotics and Mechatronics
- Master Thesis, Semester Project
| The project is a collaboration between ARSL and CVL. For acoustics, Prof. Daniel will guide you and for AI, Prof. Fisher Yu will guide you. We plan to develop a special 3D reconstruction algorithm for zebrafish larvae.
In this project, we first perform the rotation manipulation of zebrafish using an acoustically actuated capillary. Then, we would like to realize the precise 3D reconstruction of the in vivo organs of live zebrafish larvae using CV and AI algorithms. We will fabricate a microchannel chip, which can develop a single polarized vortex. By adjusting the acoustic excitation parameters, we will change the rotational speed and direction. Finally, we will program our special 3D reconstruction algorithms and software. - Acoustics and Acoustical Devices; Waves, Microbiology, Programming Languages, Robotics and Mechatronics
- Master Thesis
| In this project, we are going to develop a vision-based reinforcement learning policy for drone navigation in dynamic environments. The policy should adapt to two potentially conflicting navigation objectives: maximizing the visibility of a visual object as a perceptual constraint and obstacle avoidance to ensure safe flight. - Engineering and Technology
- Master Thesis
| Recent research has demonstrated significant success in integrating foundational models with robotic systems. In this project, we aim to investigate how these foundational models can enhance the vision-based navigation of UAVs. The drone will utilize learned semantic relationships from extensive world-scale data to actively explore and navigate through unfamiliar environments. While previous research primarily focused on ground-based robots, our project seeks to explore the potential of integrating foundational models with aerial robots to enhance agility and flexibility. - Engineering and Technology
- Master Thesis, Semester Project
| Inspired by how humans learn, this project aims to explore the possibility of learning flight patterns, obstacle avoidance, and navigation strategies by simply watching drone flight videos available on YouTube. - Computer Vision
- Master Thesis, Semester Project
| Gaussian Splatting Visual Odometry - Engineering and Technology
- Master Thesis, Semester Project
| IMU-centric Odometry for Drone Racing and Beyond - Engineering and Technology
- Master Thesis
| The first ever Mars helicopter Ingenuity flew over a texture-poor terrain and RANSAC wasn’t able to find inliers: https://spectrum.ieee.org/mars-helicopter-ingenuity-end-mission
Navigating the Martian terrain poses significant challenges due to its unique and often featureless landscape, compounded by factors such as dust storms, lack of distinct textures, and extreme environmental conditions. The absence of prominent landmarks and the homogeneity of the surface can severely disrupt optical navigation systems, leading to decreased accuracy in localization and path planning. - Engineering and Technology
- Master Thesis, Semester Project
| In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| This project seeks to leverage the sparse nature of events to accelerate the training of radiance fields. - Computer Vision
- Master Thesis, Semester Project
| The project aims to explore how prior 3D information can assist in reconstructing fine details in NeRFs and how the help of high-temporal resolution data can enhance modeling in the case of scene and camera motion. - Computer Vision
- Master Thesis, Semester Project
| The goal of this project is to develop a shared embedding space for events and frames, enabling the training of a motor policy on simulated frames and deployment on real-world event data. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| This project focuses on enhancing camera pose estimation by exploring a data-driven approach to keypoint extraction, leveraging recent advancements in frame-based keypoint extraction techniques. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| Implicit scene representations, particularly Neural Radiance Fields (NeRF), have significantly advanced scene reconstruction and synthesis, surpassing traditional methods in creating photorealistic renderings from sparse images. However, the potential of integrating these methods with advanced sensor technologies that measure light at the granularity of a photon remains largely unexplored. These sensors, known for their exceptional low-light sensitivity and high dynamic range, could address the limitations of current NeRF implementations in challenging lighting conditions, offering a novel approach to neural-based scene reconstruction. - Engineering and Technology
- Master Thesis
| Low latency Occlusion-aware object tracking - Engineering and Technology
- Master Thesis, Semester Project
| Unwanted camera occlusions, such as debris, dust, raindrops, and snow, can severely degrade the performance of computer-vision systems. Dynamic occlusions are particularly challenging because of the continuously changing pattern. This project aims to leverage the unique capabilities of event-based vision sensors to address the challenge of dynamic occlusions. By improving the reliability and accuracy of vision systems, this work could benefit a wide range of applications, from autonomous driving and drone navigation to environmental monitoring and augmented reality.
- Engineering and Technology
- Master Thesis, Semester Project
| Work on design, implementation, and validation of famous foundation models (CLIP and Segment Anything Model - SAM) in context of Event-based Segmentation. - Computer Vision
- Master Thesis
| This project focuses on combining Large Language Models within the area of Event-based Computer Vision. - Computer Vision
- Master Thesis
| Ultrasound helmets are typically used to focus ultrasound on specific regions of the brain to treat tremors. To date, most ultrasound helmets that have been developed are bulky and rigid, have suboptimal resolution, and produce considerable heat. Ultrasound arrays on flexible sheets offer an exciting new direction, but their application has so far been limited to monitoring. Importantly, no current systems are designed for manipulating microrobots within a 3D vasculature. - Biomechanical Engineering, Biosensor Technologies, Control Engineering, Electrical and Electronic Engineering, Flexible Manufacturing Systems, Mechanical Engineering
- ETH Zurich (ETHZ), Master Thesis
| Inspired by naturally-occurring microswimmers such as spermatozoa that exploit the nonslip boundary conditions of a wall, we propose here a microrobot design (a “sperm-bot”) that can execute upstream motility triggered by ultrasound. - Fluidization and Fluid Mechanics, Mechanical Engineering
- Bachelor Thesis, Master Thesis, Semester Project, Summer School
| The newly designed microrobot consists of a cavity at the center of its body within the polymer matrix. The microcavity supports an air-bubble trap, which enables propulsion in an acoustic field. - Biomechanical Engineering, Mechanical Engineering
- Bachelor Thesis, Master Thesis
| This project aims to develop a clinically usable electrode for transcutaneous vagus nerve stimulation (tVNS) therapy. The objective is to create an electrode that is biocompatible, low-impedance, and easy to use, allowing patients to apply it themselves with minimal setup time. The project involves conducting a literature review, evaluating existing designs, selecting appropriate materials, developing a prototype, and assessing its efficacy and usability in a clinical setting. The outcome will be an electrode that enhances the convenience and effectiveness of tVNS therapy, contributing to improved patient treatment adherence and outcomes. - Biomedical Engineering, Materials Engineering, Mechanical and Industrial Engineering
- Internship, Master Thesis, Semester Project
| The aim of this project is to use machine learning methods to extract useful information such as activity type and fatigue level from real-world data acquired from our textile-based wearable technology during sport activities. - Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| The aim of this project is to develop mobile software to communicate with our already developed textile-based wearable technology and process sensor data for movement monitoring. - Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| The aim of this project is to develop and improve wearable electronics solutions for data acquisition from textile-based sensors used in our smart clothing. - Engineering and Technology, Information, Computing and Communication Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| We aim to conduct a study with human participants to assess the function of our textile-based wearable technology for movement monitoring in clinical and fitness scenarios. - Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Bachelor Thesis, Internship, Master Thesis, Semester Project
| This project aims to answer the unsolved question of how to guarantee (in a computationally efficient way) hard convex constraints on the output of a network when the parameters that define the constraints change. - Geometry, Intelligent Robotics, Optimisation
- Collaboration, Master Thesis, Semester Project
| Advancing Augmented Reality Helmets for motorcyclists and racecars: Independence through Self-Localization - Engineering and Technology
- Master Thesis
| Balancing a 3D inverted pendulum using remote magnetic actuation - Electrical Engineering, Mechanical Engineering
- Bachelor Thesis, Master Thesis, Semester Project
| 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
| The process forces during machining with abrasives is critical for efficiency of the process and quality of the final product. The forces arise from different mechanisms like sliding, plowing and cutting. Although material removal occurs mostly by chip formation, a considerable portion of grinding energy goes to sliding between workpiece and dulled abrasive particles. This project aims to create a sliding force model to accurately predict forces with changing contact conditions in an abrasive process simulation. - Manufacturing Engineering, Mechanical Engineering
- Bachelor Thesis, Semester Project
| The Advanced Manufacturing Lab (am|z) is a leading hub for innovative research in advanced manufacturing and materials processing technologies, with a particular focus on advancing 3D printing processes in metals and polymers. The goal of this thesis project is to develop a numerical model for the Fused Deposition Modeling (FDM) 3D printing process. The model will be developed using the commercial finite element solver COMSOL Multiphysics. To ensure its accuracy, the numerical model will be validated against existing experimental data. - Manufacturing Engineering, Materials Engineering
- Master Thesis, Semester Project
| This dissertation project explores a novel approach to improve the accuracy and efficiency of simulations in solid mechanics, specifically in the field of powder compaction processes within powder metallurgy and materials science. It integrates Smooth Particle Hydrodynamics (SPH)[1] within the Direct Finite Element2 method[2], a widely used multi-scale analysis technique, to address the limitations of traditional Finite Element (FE) methods in handling large deformations. By combining the ability of SPH to model large strain scenarios with the advantages of FE2 for simultaneous macro- and micro-scale analyses, this research project aims to develop a comprehensive framework. This integration has the potential to revolutionize the modeling of powder behavior and aid in the development of advanced materials and manufacturing processes. - Manufacturing Engineering, Materials Engineering, Mechanical and Industrial Engineering
- Master Thesis, Semester Project
| 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
| 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. 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 air-ground localization strategies that are capable to be deployed in crop environments during the production season. The main target is to identify individual plants in the ground images using as reference the aerial images. - Agricultural Engineering, Computer Vision, Intelligent Robotics
- 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
- Master Thesis, Semester Project
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