NEW OPENING FOR A SEVERO OCHOA POSTDOC POSITION: "Machine Learning and Big Data applied to astronomical surveys, classification, and image processing"

- Estimation of stellar parameters via Machine Learning and related techniques from photometric measurements obtained by large astronomical surveys (for example ALHAMBRA and J-PLUS). The imaging data will have to be preprocessed to distinguish between different source types (star, galaxy, other) and to perform a morphological classification of the detected galaxies.
- Collaboration with the Galactic Centre group in searching for star clusters in photometric and proper motion measurements of stellar catalogues of the Galactic centre.
- Collaboration with the Galactic Centre group in searching for ways to apply machine learning and related techniques to the PSF (point source function) extraction and posterior PSF fitting photometry in extremely crowded fields.
- Contact and exchange with other groups at the IAA-CSIC who are using Machine Learning and Big Data techniques to support their efforts.
- Careful documentation of the work and of the software created, preferably in English.

Requirements:
The candidate should be a physicist with significant expertise in Machine Learning and Big Data algorithms and applications. Ideally, the candidate is a Doctor of Computer Sciences (or similar) and possesses already experience with applying these methods to astronomical images and processing of data arrays.