The Laboratory for Topology and Neuroscience (EPFL) invites applications for two postdoctoral positions in topological data analysis and machine learning affiliated with the project “Topological suite for deep learning: enhanced model reliability, stability and performance”, financed by Innosuisse (Swiss Innovation Agency) and carried out in collaboration with the Reconfigurable and Embedded Digital Systems lab (HEIG-VD) and L2F, an EPFL start-up founded in 2017 to develop innovative machine learning pipelines integrating notions from fundamental mathematics, in particular algebraic topology.
The aim of this project is to discover and develop new topological tools to unbox artificial intelligence and to enhance the robustness, performance, and reliability of deep learning models.
The postdocs selected will be members of the Laboratory for Topology and Neuroscience and collaborate with L2F and REDS.
Candidates should hold a PhD from no earlier than 2016 in mathematics, physics, or computer science, have some familiarity with topological data analysis, and be proficient Python coders.
Please send your cover letter, CV, and publication list to Kathryn Hess at firstname.lastname@example.org, to whom you should arrange for three letters of reference to be sent as well.
Starting date: 1 October 2020 or as soon as possible thereafter
Duration: 24 months
Application deadline: 30 May 2020