PhD Fellows

The SDSC funds Fellowships for PhD students currently enrolled at ETH Zürich or EPFL and supervised by a 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 enabling the adoption of data science in academia and industry.

PhD Fellows call of 2022

Student

Title

Advisor

Institution

Status

Laura Manduchi

Informed Representations: Incorporating Domain Knowledge in Deep Generative Models

ETH Zurich

Ongoing

Alexandru Tifrea

Leveraging unlabeled data for training overparameterized models

ETH Zurich

Ongoing

Luca Viano

Rethinking Optimization for Reinforcement Learning

EPFL

Ongoing

Niklas Stoehr

Uncovering Latent Entity Relationships

ETH Zürich

Ongoing

Yifan Hou

Understanding Language Models: From Knowing-That to Knowing-How

ETH Zurich

Ongoing

Kaifeng Zhao

Semantic-aware Human-scene Interaction Synthesis

ETH Zurich

Ongoing

Aditya Varre

Implicit Bias of Stochasticity and Step Size in Gradient Methods

EPFL

Ongoing

Martin Josifoski

Better Decoding Algorithms for Large Language Models

EPFL

Ongoing

Andreas Schlaginhaufen

Safe Inverse Reinforcement Learning

EPFL

Ongoing

PhD Fellows call of 2019

Student

Title

Advisor

Institution

Status

Vincent Fortuin

On the Choice of Priors in Bayesian Deep Learning

ETH Zurich

Johannes von Oswald

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

ETH Zurich

Ongoing

Xiaying Wang

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

ETH Zurich

Ongoing

Freyr Sverrisson

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

EPFL

Ongoing

Zuoyue Li

Scene Understanding for Dynamic Environments

ETH Zurich

Ongoing

Fabian Latorre

Robust Deep Learning with Generative Models

EPFL

Ongoing

Stephan Johannes Ihle

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

ETH Zurich

Ongoing

Juan Pablo Madrigal Cianci

Hierarchical Markov Chain Monte Carlo Methods for Bayesian Inverse Problems

EPFL

Jean-Baptiste Cordonnier

Unsupervised Learning for Accelerating industrial and Scientific Machine Learning Applications

EPFL

Ongoing

Clément Vignac

More with Less – Interpretable and Structured Data Science

EPFL

Ongoing