PhD Fellows

The SDSC funds Fellowships for PhD students currently enrolled at ETH Zürich or EPFL and supervised by Thesis Director from the same institution.

This call primarily targets research groups at EPFL and ETH Zürich that are working on data science and machine learning methods, broadly speaking. We invite proposals for research and development of data science methods, possibly motivated by a real-world use case, that have the potential of broadly enabling the adoption of data science in academia and industry.





Yishai Oltchik

IODEL: I/O Efficient Deep Learning

ETH Zurich

Vincent Fortuin

Bridging the Gap between Deep Learning and Probabilistic Modeling

ETH Zurich

Johannes von Oswald

Probabilistic Auxiliary Networks: Beyond Learning Single Wight Configurations in Deep Networks

ETH Zurich

Xiaying Wang

Near-Sensor Analytics and Machine Learning for Long-Term Wearable Biomedical Systems

ETH Zurich

Freyer Sverrisson

DeepSurf – a geometric deep learning approach to profile molecular surfaces for functional annotation and design


Zuoyue Li

Scene Understanding for Dynamic Environments

ETH Zurich

Fabian Latorre

Robust Deep Learning with Generative Models


Stephan Johannes Ihle

Unsupervised feature vector extraction using histogram matching, cycle-consistent Generative Adversarial Networks for bottom-up neuroscience

ETH Zurich

Juan Pablo Madrigal Cianci

Advanced Sampling techniques for data-rich Bayesian inverse problems with applications to seismology


Jean-Baptiste Cordonnier

Unsupervised Learning for Accelerating industrial and Scientific Machine Learning Applications


Clément Vignac

More with Less – Interpretable and Structured Data Science