Open Post-doctoral Position at LERMA, Observatoire de Paris
Contact Email : email@example.com. Object : MINERVA Application
The position is open within the MINERVA project (Machine Learning for Radioastronomy at Observatoire de Paris). This project brings together astrophysicists from different fields of radioastronomy. https://vm-wordpress-lerma01.obspm.fr/minerva/
Radioastronomy is experiencing an explosion of the volume of observational data with the development of giant interferometers (LOFAR, ALMA, NenuFAR, SKA). At the same time, model predictions, often resulting from HPC numerical simulations, are also producing massive data. In parallel, machine learning methods have undergone algorithmic developments that bring them to a high level of maturity.
The successful candidate is expected to work on applications of machine learning methods to the measurement and interpretation of the 21-cm signal from the Epoch of Reionization. Possible avenues of research are the application of Machine Learning to forward or backward modeling of the signal to provide alternative parameter constraints methods, the enhancement of the modeling itself (numerical simulations) with Machine Learning, exploring the use of Machine Learning to speed up the calibration of observations, the use of unsupervised learning for signal characterization, etc…The candidate is also welcome to suggest his own ideas within this general context.
The successful candidate will have access to computing ressources dedicated to MINERVA (a dedicated GPU computing node).
The LERMA/Observatoire de Paris maintains a lively visitor program and hosts regular workshops and conferences throughout the year. The successful candidate will be immersed in an internationally visible research environment in the Paris Campus, with rich intellectual and computational resources. Funding will also be allocated for travel.
– The applicants should hold a PhD in computer science or astrophysics, with either a strong expertise in Machine Learning or good knowledge of Epoch of Reionization Science and some previous experience with machine learning.
– Knowlegde of Machine Learning frameworks (Scikit-learn, Keras, etc…) and the context of HPC computing will be valued.
Conditions of application:
Applicants should submit a CV (max. 2 pages), a publication list, a short review of previous works (2 pages) and a statement of research interests (1 page). Applications should be sent via email (see above).
Applications will be reviewed from the 1st of January 2021 until the position of filled
Included Benefits: French national medical insurance, Maternity/Paternity leave, Lunch subsidies, Family supplement for children, Participation to public transport fees, Pension contributions