University of Geneva, Gravitational-Wave Science Center

Fellowship ID:University of Geneva-Gravitational-Wave Science Center-PHD4 [#24254]
Fellowship Title: PhD fellowship at ETH-Zurich on machine-learning emulators of astrophysical simulations: from stellar to cosmological scales
Fellowship Type:Academic admissions
Location:Zurich, Zurich 8092, Switzerland [map] sort by distance
Subject Areas: Astrophysics / Astrophysics and Gravitational Physics, Computational Astrophysics
Computer Science / Artificial intelligence and machine learning
Appl Deadline: finished (2023/02/01, finished 2023/10/07, listed until 2023/04/01)
Description:    

*** this fellowship has been closed and new applications are no longer accepted. ***

We invite applications for a PhD fellowship on using machine learning in order to design and build innovative emulators for complex physics simulations. This line of research is embedded into the larger context of AI for science. The methodological focus is on adaptive numerical methods and on using reinforcement learning for optimal allocation of computational resources. Concretely, we aim (i) to explore parameter spaces in stellar-binary population simulations, (ii) to embed small-scale process emulators into large-scale simulations, and (iii) to accelerate simulations of intermediate-mass blackhole formation. 

The position is hosted by the Machine Learning Institute at ETH Zurich and the successful applicant will work in Prof. Thomas Hofmann’s lab, in close collaboration with Prof. Tassos Fragos and Prof. Lucio Meyer as well as other project partners. Candidates are required to have a solid background in Deep Learning, a track record in applied projects, and strong coding skills (e.g. PyTorch). Moreover, a degree in Computer Science, Physics, Electrical Engineering or a neighboring discipline with competitive academic performance is expected. The successful candidate will need to enroll in the Computer Science doctoral program and has to fulfill the pre-requisites required by the Department. 

The review of applications will start immediately and will continue until 8/3/2023 or until the position is filled.

This position is part of GW-Learn, a multidisciplinary research program on Gravitational-Wave (GW) science, across Swiss universities. GW-Learn aims at developing techniques, theories, algorithms, and simulations that will allow for optimal knowledge acquisition from the next-generation GW observatories, such as the Einstein Telescope (ET) and the Laser Interferometer Space Antena (LISA). The successful candidates will have the opportunity to work with a team of world-leading experts in GW astrophysics, Machine Learning, GW data analysis, Cosmology, Fundamental Gravity, and  Experimental Physics, including Profs. Tassos Fragos, Michele Maggiore, Corinne Charbonnel, Antonio Riotto, and Steven Schramm from University of Geneva, Profs. Lucio Mayer and Philippe Jetzer from the University of Zurich, Profs. Thomas Hofmann and Domenico Giardini from ETH-Zurich, Dr. Elena Cuoco from the European Gravitational Observatory (EGO),  and Dr. Jonathan Gair from the Max-Planck Institute for Gravitational Physics (Albert Einstein Institute).

GW-Learn is committed to promoting diversity as a means towards the success of our missions and objectives. It is dedicated to providing a collegial, inclusive, and supportive working environment to all team members, empowering one another, and bringing excellence to our work. The hiring process and the working relationship will follow the Swiss Charter for Diversity in the Workplace adopted by our parent institutions. 

Additional opportunities within GW-Learn can be found here: https://jobregister.aas.org/ad/9216a174

Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the description.

Further Info:
https://gwlearn.unige.ch
 
24 rue du Général-Dufour 1211 Genève 4