The Swiss Data Science Center

Enabling data-driven science and innovation for societal impact, and supporting scientists, businesses and organizations in data science.
In 2017, a national Data Science initiative from the ETH Board resulted in the creation of a unique joint venture: the Swiss Data Science Center, bringing together the ETH Zürich, the EPFL, and the PSI . The Center’s mission is to accelerate the use of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, the public institutions and the industrial sector.

A team of senior data scientists and experts in domains such as personalized health & medicine, earth & environmental science, social sciences, digital humanities and economics collaborate on academic and industrial projects. This unique positioning, at the crossroad of academic excellence and a fast-paced business environment, is key to simplifying a complex data science journey.

Team

A diversified team of researchers and professionals in Data Science

Olivier Kauffmann
Olivier Kauffmann
Data Scientist
Imen Choura
Imen Choura
Head of Finance
Sabine Maennel
Sabine Maennel
Sr. Open Research Data Engineer
François Kamper
François Kamper
Sr. Data Scientist
Chiara Preti
Chiara Preti
Data Scientist
Daniel Trejo Banos
Daniel Trejo Banos
Sr. Data Scientist

Open Research

Promoting Open Research

promote open research in data science
At the SDSC, we promote open research practices throughout the entire data lifecycle.

By leveraging our expertise in semantic enablement, data governance, privacy and security, and other key areas, we help our partners and ecosystem actors to adopt open research practices that maximize the impact and reuse of their research outputs.

News

Latest news

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science
November 6, 2023

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science

Food insecurity in sub-Saharan Africa is widespread, with crop yields much lower than in many developed regions. The project aims to use laser spectroscopy to measure fluxes and isotopic composition of N2O from maize and potato crops subjected to a range of fertilization levels.
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
October 31, 2023

Street2Vec | Self-supervised learning unveils change in urban housing from street-level images

Street2Vec | Self-supervised learning unveils change in urban housing from street-level images

It is difficult to effectively monitor and track progress in urban housing. We attempt to overcome these limitations by utilizing self-supervised learning with over 15 million street-level images taken between 2008 and 2021 to measure change in London.
DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging
February 28, 2023

DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging

DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging

Optoacoustic imaging is a new, real-time feedback and non-invasive imaging tool with increasing application in clinical and pre-clinical settings. The DLBIRHOUI project tackles some of the major challenges in optoacoustic imaging to facilitate faster adoption of this technology for clinical use.

Contact us

Let’s talk Data Science

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Contact us for your next Data Science project!