Department of Information Technology and Electrical EngineeringAcronym | D-ITET | Homepage | http://www.ee.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | Department of Information Technology and Electrical Engineering | Child organizations | |
Open OpportunitiesFoundation models are a breakthrough in the field of artificial intelligence. These models are characterized by massive size, reaching billions (even trillions) of parameters, and by the ability to be adapted to a wide variety of tasks without needing to be trained from scratch. The development of these models marks a pivotal shift in AI research and application, pushing the boundaries of what machines can understand and do. However, due to the huge size of foundation model, they are very demanding in terms of computation, memory footprint and bandwidth. For this reason, foundation models face significant computational challenges. These models are typically trained on massive clusters equipped with thousands of advanced GPUs. Moreover, they require cloud services for inference as well. - Artificial Intelligence and Signal and Image Processing
- ETH Zurich (ETHZ), Master Thesis
| Non-invasive, focal drug delivery in a controlled, reliable manner can lead to breakthroughs in the treatment of brain disorders. We recently developed a unique technology based on focused ultrasounds that allows for the efficient delivery of small molecules to specific brain targets. Yet, ensuring safe and effective transmission of ultrasound energy is challenging in the presence of highly-aberrating media like the skull. This project will involve the development of closed-loop algorithms and hardware for the adaptive, real-time control of ultrasound intensities during therapy. This will ensure maximally-effective drug delivery while minimizing potential harm to the target tissue. - Engineering and Technology, Medical and Health Sciences
- Master Thesis, Semester Project
| Distributed control methodologies are iterative in nature. They rely on communication with neighbours and repeated optimization to iteratively reach the optimum. The goal of this project is to overcome the limitations of distributed control by exploring data driven methods, such as data driven control, artificial neural networks, etc. that rely on past data and strategically exploit the structure of the network to compute the optimum based on the information provided by the hubs and the external disturbances. The data driven method is designed to optimize the interactions and trading between hubs. The idea is to see if past data and interactions between the hubs can efficiently be used to accurately compute future trades. - Electrical Engineering, Systems Theory and Control, Systems Theory and Control
- Applications (IfA), Computation (IfA), Energy (IfA), Master Thesis, Theory (IfA)
| SA/MA project to design and build a 12 kW bidirectional boost converter - Electrical Engineering
- Master Thesis, Semester Project
| SA/MA project to design a Near-field RF Antenna for a Real-time Communication Channel for Control of Power Electronic Systems - Electrical Engineering
- Master Thesis, Semester Project
| Human Pose Estimation (HPE) is a task that focuses on identifying the position of a human body in a specific scene. Most of the HPE methods are based on recording an RGB image with the optical sensor to detect body parts and the overall pose. This can be used in conjunction with other sensing technologies, such as accelerometers and gyroscopes, for fitness and rehabilitation, augmented reality applications, and surveillance. - Digital Systems, Electrical and Electronic Engineering, Interdisciplinary Engineering
- Master Thesis, Semester Project
| Biological intelligence excels in adapting to new tasks by reusing prior knowledge, as seen in landmark-based navigation where animals plan routes using spatial relationships. In contrast, machine learning struggles with navigation due to challenges like understanding the effects of actions on perception (equivariance), estimating distances to objects without supervision (localization), and dealing with occlusion. The proposed method addresses these challenges by learning equivariant object representations and composing them into a coherent map, enabling accurate pose estimation and navigation. Successfully tested in a 3D simulation, the goal is to apply this approach to more realistic environments, such as drone navigation, and to demonstrate its utility in various tasks through reinforcement learning. - Artificial Intelligence and Signal and Image Processing
- Course Project
| Background Mental Workload:
Mental workload, also known as cognitive workload, refers to the mental effort and resources required to perform a specific task or activity. It encompasses various cognitive processes such as attention, memory, problem-solving, decision-making, and perception. The more demanding a task, the higher the mental workload required to perform it effectively. In the context of Human-Computer Interaction (HCI), mental workload is critical as it affects the user's ability to process information efficiently. When the workload exceeds a user's cognitive capacity, information processing slows down, leading to increased errors and reduced performance. Understanding and measuring mental workload is crucial because it affects our productivity, mental health, and overall well-being.
Background Stroke:
Stroke is a major cause of motor impairments, leading to an increased demand for effective rehabilitation methods. Technological solutions like VR-based rehabilitation and robotic movement training systems show promise in minimally supervised settings. However, maintaining patient engagement is a challenge. Balancing visual, memory, and attentional load is critical, especially for stroke patients sensitive to excessive task load. This project aims to develop an adaptive neurorehabilitation system using fNIRS to measure and adjust mental workload, enhancing patient engagement and recovery. - Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Bachelor Thesis, Collaboration, Internship, Lab Practice, Master Thesis, Semester Project
| This project investigates the relationship between corticomuscular coherence (CMC) and both muscular and cognitive fatigue during dual-task performance. Using synchronized EMG and EEG data collected via the Quattrocento system from OTBioelettronica, we aim to explore CMC variations at the onset of these signals during solo and dual motor-cognitive tasks. By analyzing data from motor (e.g., reach-and-grab) and cognitive (e.g., reading) activities, we will develop algorithms for automatic detection of EMG and EEG signal onsets. The goal is to validate whether CMC coherence increases during dual tasks, contributing to interventions for fatigue management and cognitive-motor training. This research, in collaboration with IIT Genoa and led by Dr. Marianna Semprini, seeks to enhance our understanding of cognitive-muscular interactions through CMC. - Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Master Thesis
| The remarkable complexity of morphogenesis and tissue regeneration implies the existence of a transcellular communication network in which individual cells sense the environment and coordinate their biological activity in time and space. To understand the fascinating ability of tissue self-organization, comprehensive study of biophysical properties (cellular nanomechanics such as tension forces and bioelectromagnetics) in combination with the analysis of biochemical networks (signaling pathways and genetic circuits) is required.
In this framework we are investigating the unacknowledged key role of Desmoglein 3 (Dsg3) as a receptor involved in mechanosensing, capable of initiating a signaling response in the transcellular communication network, which results in stem cell fate conversion, plasticity and tissue repair.
Our goal is to apply innovative Fluidic Force Microscopy to measure altered biophysical parameters upon disruption of Dsg3 transadhesion such as cell stiffness, cell-cell adhesion, cell surface charges and electric potentials. Together with the University of Bern and University of Lübeck we are further investigating how these biophysical changes relate to transcriptomic, epigenomic and proteomic response circuits to ultimately infer biophysical and biochemical circuits involved in Dsg3 signaling.
- Biochemistry and Cell Biology, Biomedical Engineering, Medical and Health Sciences, Physics
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis, Semester Project
|
|