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Combine two exploding fields in computer science: machine learning and agent-based modelling.
Based on preclinical and in vitro studies of cell behaviour and cytokine reaction-diffusion and mechanical tests we have generated an in-house biofidelic agent-based model of the human skeleton and its response to diseases and their treatments. This model reproduces the effects of several widely used osteoporosis treatments on key parameters used to quantify fracture risk. This rule-based approach involves studying bone mechanobiology at the cell scale and extrapolating this to millions of cells at the tissue scale to understand the pharmacokinetics of treatments and identify possible new therapies and approaches to patient-specific treatment.
An alternative approach to in silico prediction of response to treatment is a supervised learning approach where we simply input baseline and follow-up bone scans to a CNN with twelve layers constructed using keras. We then attempt to dive into the black box and quantify what characteristics of the input govern the response of our model. The issue is the clinical data is not big enough to do this well so we use the agent-based model as input to the ML approach to construct a proxy model! This also helps us understand, validate and quantify the uncertainty in the agent-based model. To decide which runs of the agent-based model to use as input to the ML approach to construct the proxy model we use polynomial chaos expansion. - Animal Physiology-Cell, Artificial Intelligence and Signal and Image Processing, Cell Development (incl. Cell Division and Apoptosis), Cellular Interactions (incl. Adhesion, Matrix, Cell Wall), Computation Theory and Mathematics, Modeling and Simulation, Protein Targeting and Signal Transduction
- Bachelor Thesis, Master Thesis, Semester Project
| Problem:
Accurately estimating the weight of food items is a significant challenge in healthcare applications. While state-of-the-art 3D cameras can precisely measure food volume, the lack of datasets with labeled food densities remains a major obstacle for accurately determining food amounts.
Goal of the thesis:
The thesis aims to create a dataset that includes the volume, weight, and 3D scans of various food items using a state-of-the-art structured light camera. Due to the vast variety of foods, compiling a comprehensive dataset is impractical. Therefore, the project will also include training and testing a machine learning model to predict the densities of food items that were not seen during its training.
- Food Engineering, Food Processing, Health Information Systems (incl. Surveillance), Nutrition and Physiology
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis, Semester Project
| This moonshot project focuses on researching and exploring the potential of flexible and printable electronics, fabrication technologies, and applications in wearables based on the Voltera NOVA printer. Tasks will include ECAD and MCAD design, manufacturing, and prototype testing. - Electrical and Electronic Engineering, Printing Technology
- Bachelor Thesis, Biomedical (PBL), Energy Harvesting (PBL), Master Thesis, PCB Design (PBL), Semester Project, Wearables (PBL)
| Extend the recent Marigold in different aspects - Computer Vision
- Master Thesis
| This project focuses on designing testbeds for self-sustainable IoT sensors, specifically targeting solar and thermal energy harvesting. Tasks will include ECAD and MCAD design, firmware development, and prototype testing. - Electrical and Electronic Engineering, Mechanical Engineering
- Bachelor Thesis, Energy Harvesting (PBL), Firmware (PBL), Master Thesis, Microcontroller (PBL), PCB Design (PBL), Semester Project, Software (PBL)
| Digital tools, including smartphone apps and conversational agents, hold great promise for enhancing cancer supportive care. These apps can measure patients' symptoms, offering personalized advice on supportive care interventions (e.g., physiotherapy, psychologist, nutritionist) and improving access to such services for cancer patients. Despite the effectiveness of digital health interventions, there is currently a gap in automatic conversational agents that assess symptoms and provide advice on supportive care treatment for cancer patients. This project aims to evaluate the potential of Large Language Models in creating a conversational agent capable of assessing symptoms in cancer patients and offering personalized advice on cancer supportive care. - Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Medical and Health Sciences
- Lab Practice, Master Thesis, Semester Project
| Digital tools, such as smartphone apps, offer promising opportunities to enhance cancer supportive care. Smartphone apps can measure patients' symptoms and provide personalized advice on supportive care interventions. However, to date, a patient-centric approach (i.e., co-designing an app with users) is lacking in the development of such apps. By co-designing the app with users, potential barriers to implementation, such as engagement and usability, can be addressed, leading to increased adherence and a more sucessful implementation. The aim of this project is to use a patient-centric approach to design and develop a smartphone app for cancer patients. - Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Lab Practice, Master Thesis, Semester Project
| Are you fascinated by the untapped potential of wearables and smartphone apps in revolutionising the way we manage and treat chronic conditions? This project offers an unparalleled opportunity to dive deep into the world of digital health, exploring how mobile technologies can enhance remote assessment and deliver impactful interventions for conditions ranging from mental health to heart disease, and beyond. - Behavioural and Cognitive Sciences, Medical and Health Sciences
- Bachelor Thesis, Master Thesis, Semester Project
| In this project, we aim to develop a visualization tool designed for rendering and interacting with 3D human motion and scenes. - Computer Graphics, Computer Software, Computer Vision, Engineering and Technology, Virtual Reality and Related Simulation
- Bachelor Thesis, Semester Project
| This project aims to develop advanced
earthquake forecasting models using
bio-inspired Spiking Neural Networks (SNNs).
By exploiting the inherent flexibility of SNNs,
the project will create sparse, multi-step
forecasting models capable of integrating
data from various sources. These models will
be built and tested using the NEST neural
simulator, emphasizing neuroplasticity,
neuromodulation, and neural Darwinism
principles. The goal is to enhance the
efficiency of earthquake predictions by
learning more effectively from limited and
lower-quality data, potentially leading to
significant improvements in forecasting
methods and ultimately reducing the risks
associated with seismic events. - Earthquake Seismology, Knowledge Representation and Machine Learning, Neural Networks, Genetic Alogrithms and Fuzzy Logic
- ETH Zurich (ETHZ), Master Thesis, Semester Project
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