SDSC 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.
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
EPFL
Safe Inverse Reinforcement Learning
EPFL
Better Decoding Algorithms for Large Language Models
EPFL
Implicit Bias of Stochasticity and Step Size in Gradient Methods
ETH Zurich
Semantic-aware Human-scene Interaction Synthesis
ETH Zurich
Understanding Language Models: From Knowing-That to Knowing-How
ETH Zürich
Uncovering Latent Entity Relationships
ETH Zurich
Informed Representations: Incorporating Domain Knowledge in Deep Generative Models
ETH Zurich
Leveraging unlabeled data for training overparameterized models
EPFL
Rethinking Optimization for Reinforcement Learning
PhD Fellows call of 2019
Ongoing
More with Less – Interpretable and Structured Data Science
Ongoing
Unsupervised Learning for Accelerating industrial and Scientific Machine Learning Applications
Thesis Defended
Hierarchical Markov Chain Monte Carlo Methods for Bayesian Inverse Problems
Ongoing
Unsupervised feature vector extraction using histogram matching, cycle-consistent Generative Adversarial Networks for bottom-up neuroscience
Ongoing
Robust Deep Learning with Generative Models
Ongoing
Scene Understanding for Dynamic Environments
Ongoing
DeepSurf – a geometric deep learning approach to profile molecular surfaces for functional annotation and design
Ongoing
Near-Sensor Analytics and Machine Learning for Long-Term Wearable Biomedical Systems
Thesis Defended
On the Choice of Priors in Bayesian Deep Learning
Ongoing
Probabilistic Auxiliary Networks: Beyond Learning Single Wight Configurations in Deep Networks
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