Student Assistant - Data Analysis for Greenhouse Gas Accounting
Accounting & Finance, Data Science · Intern
Zürich, Switzerland
Student Assistant – Data Analysis for Greenhouse Gas Accounting
20%, Zurich, fixed-term
The ETH Office of Sustainability is seeking a motivated student research assistant to support the improvement of greenhouse gas (GHG) emission accounting methodologies at ETH Zurich from December 2026 until March 2027. The GHG emission accounting is a core aspect of the ETH Net Zero Programme. A holistic and accurate emission accounting serves as a basis to take impactful decisions on the path to net zero emissions at ETH.
The applications will be evaluated on a rolling basis and interviews scheduled accordingly. The ideal start date is December 1, 2026.
Project background
ETH has a long history of reporting GHG emissions, in particular emissions in Scope 1 (direct emissions on campus), Scope 2 (indirect emissions through purchased energy such as electricity and district heating) as well as emissions from business travel (part of Scope 3 emissions). Since 2023, ETH has been publishing a holistic emission inventory including Scope 3 emissions. The reported emissions can be found in this online dashboard and details on the methodology in the following article. A large share of the Scope 3 emissions remains estimated based on the annual spending in certain categories, which results in limited accuracy and makes it challenging to decide on reduction measures and track their effects.
The main objective of this position is to develop and test approaches for matching existing procurement data with emission factor databases to improve the accuracy of the Scope 3 emission inventory. The work involves data processing, automation, and the application of machine learning techniques to improve the efficiency and accuracy of Scope 3 emissions calculations.
Job description
- Update existing Python script to extract the most up to date and accurate spend-based emission factors from the underlying databases.
- Improve, develop, and benchmark approaches (in Python) for matching procurement data (mostly text descriptions) with emission factor databases. This includes classical text matching approaches as well as approaches based on machine learning / embeddings / etc.
- Implement the final approach in a reproducible manner which can be applied annually for the GHG emission accounting.
- Include supplier specific emission data in the GHG emission accounting and investigate approaches to efficiently integrate publicly available emission databases (optional).
Profile
- Enrolled student at ETH Zurich or another Swiss university
- Strong programming skills (particularly in Python) and software-development experience
- Interest and experience in data analysis and handling large datasets
- Knowledge of machine learning methods, large language models, and their practical application
- Experience with R is a plus
- Interest in sustainability, life cycle assessment (LCA), or GHG accounting is a plus
- Independent, structured, and solution-oriented way of working
Workplace
Workplace
We offer
- An opportunity to contribute directly to ETH Zurich's net zero goals and Net Zero Programme
- Hands-on experience at the intersection of sustainability, data science, and environmental assessment
- Regular 1-on-1 meetings and mentoring
- Collaborative team with diverse backgrounds
We value diversity and sustainability
Curious? So are we.
We look forward to receiving your online application by November 30 with the following documents:
- A short motivation letter (max. 1 page)
- Your current CV
- Your academic transcripts
- A brief portfolio or code sample demonstrating relevant work (e.g. GitHub link)
Further information about ETH Sustainability can be found on our website. Questions regarding this job opportunity should be directed to Simon Muntwiler at simon.muntwiler@sl.ethz.ch (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.