Found 14 relevant results in 3.15s where lecturer="Irena Hajnsek"

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102-0617-00L 2020W , 2021W , 2022W , 2023W , 2024W , 2025W , 2026W 3 Credits MSC D-BAUG , D-MAVT , D-PHYS , D-ERDW , D-ITET

The course will provide the basics and principles of Radar Remote Sensing (specifically Synthetic Aperture Radar (SAR)) and its imaging techniques for the use of environmental parameter estimation.

2020W
2021W
2022W
2023W
2024W
2025W
101-0522-10L 2020W , 2021S , 2021W , 2022S , 2022W , 2023W , 2024W , 2025S , 2026S 1 Credits DR D-BAUG

Current research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations.

2020W
2021S
2021W
2022S
2022W
2023W
2024W
2025S
102-0675-00L 2020W , 2021W , 2022W , 2023W , 2024W , 2025W , 2026W 4 Credits BSC , DR , MSC D-BAUG , D-MAVT , D-PHYS , D-ERDW , D-ITET , D-USYS

The aim of the course is to provide the fundamental knowledge about earth observation sensors, techniques and methods for bio/geophysical environmental parameter estimation.

2020W
2021W
2022W
2023W
2024W
2025W
102-0675-AAL 2021W , 2022S , 2022W , 2023S , 2023W , 2024S , 2024W , 2025S , 2025W , 2026S , 2026W 4 Credits MSC D-BAUG

The aim of the course is to provide the fundamental knowledge about earth observation sensors, techniques and methods for bio/geophysical environmental parameter estimation.

2021W
2022S
2022W
2023S
2023W
2024S
2024W
2025S
2025W
2026W
102-0515-01L 2005W , 2006W , 2007W , 2008W , 2020W , 2021W , 2022W , 2023W , 2024W , 2025W , 2026W 3 Credits BSC D-BAUG

The course is organized in the form of seminars held by the students. Topics selected from the core disciplines of the curriculum (water resources, urban water engineering, material fluxes, waste technology, air polution, earth observation) are discussed in the class on the basis of scientific papers that are illustrated and critically reviewed by the students.

2005W
2006W
2007W
2008W
2020W
2021W
2022W
2023W
2024W
2025W
101-0523-15L 2024W , 2025S , 2026S 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

2024W
2025S
101-0523-14L 2023W 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-10L 2020W 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-11L 2021S 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-12L 2021W 1 Credits DR , MSC D-ARCH , D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-13L 2022W 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

Introduction into Environmental Engineering

Einführung Umweltingenieurwissenschaften

102-0004-00L 2022W , 2023W , 2024W , 2025W , 2026W 3 Credits BSC D-BAUG

In this course students are introduced to how environmental problems in the areas of water quantity and quality, waste production and recycling, air pollution control, are formulated and solved with engineering methods. The course makes a connection between the theoretical Bachelor foundation classes and practical topics of environmental engineering in six main thematic areas.

2022W
2023W
2024W
2025W
102-0510-00L 2023S , 2024S , 2025S , 2026S 3 Credits BSC D-BAUG

Typical parameter which are relevant for environmental systems are measured with modern sensors. The measuring devices are put together and programmed using a Sensebox (Arduino-based measuring box). The environmental data are analyzed and compared with information from literature.

2023S
2024S
2025S
102-0617-01L 2020S , 2021S , 2022S , 2023S , 2024S , 2025S , 2026S 3 Credits MSC D-ITET , D-BAUG , D-MATH , D-ERDW , D-MAVT , D-PHYS

The aim of this course is to get an overview of several methodologies/algorithms for analysis of different sensor specific information products. It is focused at students that like to deepen their knowledge and understanding of remote sensing for environmental applications.

2020S
2021S
2022S
2023S
2024S
2025S