PhD: Robust detection of astronomical sources using convolutional neural networks (ERC, Idex, CNES)

The goal of this thesis is to address the problem of detecting and deblending sources by means of deep convolutional neural networks. This approach has proved its great potential during recent exploratory work by our team (Paillassa & Bertin 2016).
In contrast to machine learning techniques that have already been applied to astronomical data, the aim here will be to define and apply a multi-instance pixel labelling method directly from a heterogeneous set of multichannel images, relying on state-of-the-art techniques in the field. The sky background and the high dynamic range that characterize astronomical images will have to be taken into account.
This research work will require the development of a data augmentation procedure adapted to the problem (multi-epoch and multichannel content) and the manipulation of a large volume of image simulations and real observations in an intensive distributed computing environment, using the latest generation graphical computing (GPU) processors at the Laboratory for Astrophysics in Bordeaux and Institute for Astrophysics of Paris. The work on simulations and data will be done in close collaboration with the Euclid and CFIS teams at IAP (for low density fields), and Cosmic-DANCe in Bordeaux (high density fields, http://www.project.dance.com). The final aspect of this thesis will be the statistical validation of the models and algorithms on Euclid and ground image simulations as well as their application to actual Cosmic-DANCe and CFIS survey data.
It is a joint-PhD thesis between the Laboratoire d'Astrophysique de Bordeaux and the Institut d'Astrophysique de Paris. The student will be based at the University of Bordeaux, with regular visits at the IAP.
LAB and IAP offer very stimulating research environments with staff working in various areas of astrophysics and image processing. As a member state of ESO, ESA and CFHT, France has access to their first-class facilities. The beautiful city of Bordeaux offers one of the highest quality of living and a vibrant cultural life.
Funding is fully secured for the 3 years of the PhD, 50% by the CNES and 50% by an "Initiative of Excellence" grant at the University of Bordeaux.
Requirements, skills, qualifications:
- Degree: Aspiring candidates must hold a degree equivalent to a European Master (5 years of Higher Education) or engineering degree, in signal/image processing, or machine learning or astrophysics, or related fields.
- Programming: Experience with Python is welcome
- Experience in image processing is a plus, although all excellent applicants will be considered
- Language: Proficiency in either English or French is required
- Nationality: All nationalities are welcome to apply (subject to visa restrictions)
Creativeness and motivation are especially welcome.
Review of applications starts April 1, 2017, for a start early/mid September 2017.
The Doctoral studies require no more than three years.