Department of Civil, Environmental and Geomatic EngineeringAcronym | D-BAUG | Homepage | http://www.baug.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | Department of Civil, Environmental and Geomatic Engineering | Child organizations | |
Open OpportunitiesTime-series data is increasingly prevalent across various domains, including finance, healthcare, and environmental monitoring. The ability to extract meaningful information from time-series data is crucial for prediction, classification, and anomaly detection. This project focuses on exploring different time-series representations and their impact on machine learning tasks. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| A machine learning-based classifier will be developed to automatically detect abnormal GNSS ephemerides in daily broadcast ephemeris files. - Earth Sciences
- ETH Zurich (ETHZ), Semester Project
| A dynamic tropospheric process noise model will be implemented into GNSS real time kinematic (RTK) algorithms to improve the estimation of drone-based GNSS zenith total delays (ZTDs). - Earth Sciences
- ETH Zurich (ETHZ), Semester Project
| This thesis explores ionospheric modeling using GNSS and potentially VLBI data, employing Gaussian Process regression to address the non-linear behaviors and noise inherent in such data. The study focuses on enhancing predictive accuracy and the quantification of uncertainties in ionospheric variations, which are essential for improving global navigation and communication systems. - Earth Sciences
- Bachelor Thesis, ETH Zurich (ETHZ)
| This master thesis aims to improve the retrieval of soil moisture using the GNSS Interferometric Reflectometry (GNSS-IR) method by the development of a new, machine-learning based model for the correction the vegetation influence on the retrieval. - Earth Sciences
- ETH Zurich (ETHZ), Master Thesis
| This master thesis aims to explore the application of Physics-Informed Neural Networks (PINNs) to regional geoid modeling. PINNs integrate physical constraints into neural network architectures, offering a novel approach to accurate geoid modeling while maintaining interpretability. - Earth Sciences
- ETH Zurich (ETHZ), Master Thesis
| In this study, the student should apply deep learning algorithms to segment the measurements from the Surface Water and Ocean Topography satellite mission, specifically focusing on inland water bodies. The outputs of this study may contribute to inland water detection during flood events and also potentially to refining the pre-defined water body shapes. - Earth Sciences
- ETH Zurich (ETHZ), Master Thesis
| In this study, the student should develop a generative model to separate the total signals measured by GRACE(-FO) satellite missions into the contributions of individual water storage components. The results will be evaluated by comparing them with independent in-situ and satellite-based storage observations. The findings of this study will contribute to a better understanding of the terrestrial water cycle from a global perspective. - Earth Sciences
- ETH Zurich (ETHZ), Semester Project
| This work will explore the potential of greatly increasing the number of VGOS sessions per month by limiting the amount of recorded data. - Earth Sciences
- ETH Zurich (ETHZ), Semester Project
| Landslides are a dangerous natural hazard affecting the lives of people all over the world. This thesis will focus on the detection of paraglacial landslides from a global glacier-velocity dataset. The behavior of these sites throughout time will be analyzed and compared to an existing landslide database. - Geomorphology, Glaciology
- Master Thesis
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