Projects
Institute of Computational Life Sciences
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Stability of self-organizing net fragments as inductive bias for next-generation deep learning
We recently released "A Theory of Natural Intelligence", proposing a possible key to the emergence of intelligence in biological learners. Goal of this fellowship is to develop a technical implementation of the concept of self-organizing netfragments within contemporary deep artificial neural nets. ...
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Drone Signal Dataset
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Maximizing the Benefits of Organic Fertilizers: A Data-Driven Approach to Improve Efficiency and Reduce Pollution
The project aims at assessing the use of soil enzymatic activity to improve the use efficiency of nitrogen in agriculture to fulfill environmental goals and climate targets. We work together with ETH spin-off Digit Soil who already developed a device measuring on-site biological activity, an important soil process ...
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Stroke DynamiX
Stroke DynamiX explores data-driven stroke management using machine-learning techniques in a consortium of statistics researchers, translational enablers, and clinical partners. We implement statistical tools to dynamically model stroke epidemiologically and predict real-time sepsis onset in the clinic. ...
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Automatic individualization of mathematics tasks
In this project, the exercises provided to the students are to be automatically individualized. For this purpose, the exercises and exams solved by the students are analyzed and the and the missing competences will be identified. This approach should generate exercises optimized for each student and therefore ...
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Construction kit online didactics
Due to the limited resources of rooms and the increased demand for offers of part-time study, the online part of teaching and the quality of e-learning didactics in online courses are becoming more and more important. In the project "Building Blocks for Inspiration in Online Didactics", lecturers from diverse ...
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Digital Health Zurich – A practice lab for patient-centred clinical innovation
Digital Health Zurich researches digital health solutions in the hospital context and implements them efficiently and with practical relevance. Core topics are Patient Reported Outcome Measure (PROMs), remote monitoring, integrated care and related technologies as well as empowerment of patients and staff. Our ...
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Data and Team Sports
The project will assess the feasibility of automatic quantification of two very important aspects of a team game: fatigue, and quality of decision making. If successful, we intend to develop the idea further by creating a well-coordinated consortium of academics and companies in Switzerland and create a cutting edge ...
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Digital Physio: prescriptive rehabilitation for pediatric patients with cerebral palsy
This project aims at creating a machine learning-based tool for predicting success in functionalmobility treatment with Lokomat device for children with cerebral palsy (CP).
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Compensation Study Universities of Applied Sciences Switzerland 2023
Every two years, FH SCHWEIZ conducts a cross-sectional and longitudinal study in all national languages (G, F, I) among its members and graduates of universities of applied sciences on the topic of salary and its influencing factors, as well as other supplementary key topics. The data collection is designed as a ...
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Modeling of multicentric and dynamic stroke health data
The aim of the project is the further development of probabilistic and dynamic modeling of diseases in the form of Bayesian networks in the field of digital health as a central strategic pillar of the newly formed specialist group "Medical Image Analysis and Data Modeling" of the "Computational Health" focus. To ...
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AI for colorectal cancer: towards the improved CMS classification and interpretability
Access to large complex biomedical data today allows scientists to take full advantage of AI-driven approaches in a variety of applications with high societal impact. One such application is precision medicine, which is gradually becoming reality for some cancers. Unfortunately, for colorectal cancer (CRC) ...
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Investor and Stakeholder Tools for Tracking Companies’ Climate Commitments, Greenwashing and ESG Trends
In this project, we are developing science-based methods to systematically identify potential greenwashing in corporate communications as well as signals of green innovations and technologies. By applying advanced AI methods to thousands of stocks, we aim to develop novel, ESG-compliant financial products. ...
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Feature Learning for Bayesian Inference
The goal of this project is to use interpretable Machine Learning (ML) to find low-dimensional features in high-dimensional noisy data generated by (i) stochastic models or (ii) real systems. In both cases, the problem is to disentangle the effect of high-dimensional disturbances, such as noise or unobserved inputs, ...
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OSR4H – Open Set Recognition for Hematology
Development of a Proof of Concept for visual Open Set Recognition (OSR) algorithms applied to a Hematology task, the classification of white blood cells.
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An experimental framework to allow evidence-based sustainability policymaking
This research project evaluates the feasibility of using a mathematical decision framework (based on the 2019 Nobel laureates in Economy who developed an experimental approach for improving policy in poverty through field experiments) in sustainability policy, and achieving a software-supported and data-driven ...
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Drone Alarm
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Food Chain Model for sustainable food chains and food waste prevention
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AWARE
The project AWARE aims to build a platform together with students and colleagues of the ICLS on the topics of sustainable studying in Wädenswil: everyday life design, use of resources, nutrition, housing and living. The contents of the platform are to be developed independently by students of the master course Food ...
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Deep Brain Vessel Profiler
The architecture of the supplying brain blood vessels is believed to impact the occurrence and severity of common cerebrovascular diseases such as ischemic strokes or intracranial aneurysms. In this project, we study methods to efficiently quantify the variability in human cerebral vasculature and how this ...