Postdoc in TDA at EPFL

The Laboratory for Topology and Neuroscience (EPFL) and L2F—Learn to Forecast (EPFL Innovation Park) invite applications for two postdoctoral positions in topological data analysis and machine learning, funded by the project “Topological Warning Signals for Critical System Transitions” supported by Innosuisse (Swiss Innovation Agency).

The aim of this project is to develop original predictive methods, combining advances in topological data analysis with machine learning research. The first goal of the project, inspired by critical phase transitions, is to define a new type of early warning signal using applied topology, in order to better anticipate sudden changes of regime such as financial crashes, earthquakes, or epileptic attacks. The second goal concerns the creation of new local features by analyzing the topology of datasets, in order to generalize current topological data analysis techniques to enhance the performance of machine learning models. 

L2F is a research-driven company that offers mathematically sophisticated machine learning solutions to corporate and institutional clients. L2F’s researchers, who won the 2017 NYC Taxi Challenge on Kaggle, are motivated by the desire to understand the fundamental laws of machine learning, in terms of its underlying mathematical theory. L2F is one of the fastest growing Swiss companies, with already 30 employees after one year of existence, and provides an exciting environment for multi-disciplinary researchers.

The appointed candidates will be based at L2F on the EPFL Innovation Park and engage in state-of-the-art research, in collaboration with the Laboratory for Topology and Neuroscience.

Candidates should hold a PhD in one of the following areas: algebraic topology, theoretical physics, or machine learning. Please send your cover letter, CV, and publication list to Kathryn Hess at  and Maxime Gabella at, to whom you should arrange for three letters of reference to be sent as well.

Starting date: March 1, 2019, or as soon as possible thereafter.

Duration: 18 months.

Application deadline: January 1, 2019.

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