The University of Geneva Particle Physics and Computer Science Departments invite applications for
to work on an interdisciplinary project to provide High Energy Physics (HEP) and solar astronomy with robust deep density machine-learning (ML) tools with focus on predictive, generative and anomaly detection models, with the ultimate objective to maximise the LHC’s sensitivity to discover physics beyond the Standard Model, as well as to optimise solar flare prediction.
The project provides funding for a total of 12 positions composed of PhD students and postdocs for 4 years (so-called SNSF Sinergia project) and is led by 4 internationally recognised experts in their field representing a unique pool of interdisciplinary competences: Francois Fleuret (Geneva, data science) is an expert in deep learning techniques and simulation; Slava Voloshynovskiy (Geneva, data science) brings expertise in theoretically explainable ML; Tobias Golling (Geneva, particle physics) is an expert in LHC physics; and André Csillaghy (Fachhochschule Nordwestschweiz, solar physics) is one of the leading experts in the domain of high-energy solar astronomy.
The successful postdoc candidates have (or soon receive) the equivalent of a PhD degree with a specialisation in one or more of the areas of
(i) computer/data science
(ii) particle physics
(iii) solar astronomy
The successful candidates will be co-supervised by a physics and a ML expert and have the opportunity to be part of and shape this interdisciplinary project spanning all the way from the development of theoretical ML foundations to their practical applications and generalisation in real-world science questions in the above-mentioned domains.
The post includes teaching duties and supervision of undergraduate and PhD students, as well as opportunities for outreach work. Non-francophone candidates are encouraged to achieve proficiency in French during their first year of studies.
To apply please fill out https://script.google.com/macros/s/AKfycbzsAuvln8ztuzWkd4mExNli6uexR2viOnNIA2TtQi0-7dCuB0WE/exec?hl=en and upload a CV, a motivation statement, and provide three reference letters to . Applications should be received by August 17 2020 and the position is expected to start by January 2021 (possibly earlier if requested). For further information please contact .