Education

Education

Tutorship for MSc Students

In general, each MSc D-MTEC Master student is required to select a professor from the D-MTEC as his/her/its tutor, ideally in the second semester. This tutor is responsible for approving the student’s supplementary course selection of the learning agreement. However, the supervision of the master thesis is not mandatory for the tutor. We refer to the MSc MTEC Regulations for details on the tutorship.

Please start by visiting our website to learn more about our research and, in particular, our labs. In general, we are looking for students with a strong technical background, especially in the fields of computer science and/or data analytics (data mining/machine learning).

For your application documents, please provide the following information in advance:

  • Cover letter including a short motivational statement and your preferred lab,
  • CV, and
  • proposition of only supplementary courses (e.g., courses that begin with 363-XXX are not allowed since they refer to your main courses).

Please send your application documents to . Once we receive your application, we will forward your application to your preferred lab. Please note that incomplete or formally incorrect applications will not be reviewed. If we accept the tutorship, we will notify you by email.

As stated in the regulations, a tutorship is not offered for MAS students at D-MTEC.

We are looking for students with a strong technical background, especially in the fields of computer science and/or data analytics (data mining/machine learning). We do not require any mandatory courses. However, we strongly recommend taking one or more of the courses offered by our group, or courses related to the field of data analytics (e.g. 252-0220-00 Introduction to Machine Learning, 252-0535-00 Advanced Machine Learning, etc.).

If you are interested in writing your thesis with us, we would be happy to receive your applica- tion, consisting of a CV, a transcript of records, and a motivation statement outlining your topic of interest and identifying the potential host. For a list of possible topics, please check our webpage and contact the responsible person directly.
 

Semester and Thesis Projects

Master's Thesis / Semester Project at ETH Zurich: Energy Savings and Load Shifting of Customer Demand under Dynamic Grid-usage Tariffs

Showcasing energy savings and load shifting potential of residential customers' electricity demand under time-dependent grid-usage tariffs. Show details 

Master Thesis at ETH Zurich in Applied Computer Science: Development of an ML Algorithm for Drunk Driving Detection

Driver state detection systems will become mandatory in many countries in this decade. In this thesis, you will use a unique multi-sensor dataset with 55 drivers collected by us in a in a real car on a test track to develop a ML drunk driving detection algorithm! Show details 

Estimating house energy efficiency based on large-scale field study on heating systems

The goal of this thesis is, based on heat pump operational data, to create a model capable of extracting context information needed for heating system analysis. This approach offers significant potential for energy savings and wider renewable energy adaptation, as equipment manufacturers would be enabled to learn from operating equipment data and provide the homeowner with optimized heat pump settings, which would lead to energy savings for homeowners. Show details 

Optimizing residential heating: a data-centric approach to heat pump implementation

As the world shifts towards renewable energy sources, heat pumps have emerged as a critical technology for reducing reliance on fossil fuels in building heating systems. These pumps, which draw energy from air or ground sources, represent a significant advance over traditional fossil fuel heating methods. Yet, not all buildings are currently suited for direct transition to heat pump technology; some require retrofitting to optimize energy efficiency, while others are already efficient enough to make the switch. The challenge lies in accurately identifying which houses are ready for heat pump implementation and which ones need retrofitting. Current heat systems, including gas boilers, offer a wealth of data, but lack crucial building context information like size, insulation, room temperatures or heating system hydraulic architecture. This gap in data hinders the effective analysis and optimization of heating systems. This thesis aims to develop a model using gas boiler data to deduce building context information. The goal is to identify homes ready for heat pump implementation, streamlining the transition towards more sustainable heating solutions. Show details 

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