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| One Postdoctoral Contract Big Data Science in Astronomy 2018 | Closing date: 2018-08-30
Contact: Secretariat of the Research Division |
The IAC (Tenerife) invites applications for ONE postdoctoral contract to work on developing pattern recognition tools to be applied to large imaging surveys within a research project funded by the Fundación BBVA, and in the wide context of the EU-funded SUNDIAL project (H2020-721463), both led at the IAC by Drs. Johan Knapen and Ignacio Trujillo.
Research topics at the IAC include most areas of astrophysics: solar physics, planetary systems, stellar and interstellar physics, galaxy formation and evolution, and cosmology and astroparticles. | | ▸ more | The selected candidate will work in a growing group which aims to use computer science and machine learning tools to advance the study of the morphology and structure of galaxies from existing and upcoming deep imaging surveys. The group is led by Knapen and Trujillo and further consists of various PhD students and postdocs. The group uses deep imaging obtained with various telescopes, uses data from several published surveys, is formally involved in the LSST project and, through the IAC?s overall involvement, plans to use Euclid deep imaging data. The group forms part of the EU-funded ITN SUNDIAL (https://www.astro.rug.nl/sundial) is an ambitious interdisciplinary network of nine research groups in The Netherlands, Germany, Finland, France, the United Kingdom, Spain, Belgium and Italy. The aim of the network is to develop novel algorithms to study the very large databases coming from current-day telescopes to better understand galaxy formation and evolution, and to prepare for the huge missions of the next decade. The postdoc is expected to work closely with the group members at the IAC as well as the SUNDIAL researchers, staff and PhD students in computer science and astronomy.
Duties: The selected candidate will be mainly devoted to develop pattern recognition tools based on new algorithms in the general area of machine learning to be applied in the context of imaging from the LSST telescope. The aim is to detect, in an automated way, structure in deep imaging of the night sky. After a period of supervised learning, the software should be able to automatically construct models of the scattered light (point spread function) from stars in the images, and to detect and characterise emission from Galactic cirrus. To prepare the arrival of LSST data, the training data will come from a variety of imaging surveys: SDSS, Stripe82, DECALS and HSC SSP. In addition to the creation of the algorithms and software tools, the candidate is expected to publish a number of articles describing the new software and the discoveries made with such applications.
Position requirements: The successful candidate must have a good publication record and provide evidence of experience in leading original research.
Qualification requirements: To be eligible for admission, applicants must have obtained a Ph.D. degree in Astrophysics, Physics or Computer Science, by the application deadline (August 31, 2018). A copy of the degree or corresponding stamped certificate (issued by the University where the degree was obtained) must be included. Applicants who have not completed their Ph.D. degree within the application deadline will not be considered.
Selection stage: The selection process will consist of two stages: a merit assessment phase and an interview.
In the merit assessment stage, the Selection Committee will verify and rate the merits documented by the applicants. The maximum score that can be awarded for the assessment of merits will be 80 points, which will be calculated as the sum of the scores obtained in each of the sections.
A) Maximum score: 60 points
? Scientific and technical contributions to the field of research.
? Scientific publications, in quality and quantity, listed in the Science Citation Index (SCI conference contributions, teaching tasks, participation in scientific committees, membership of scientific conference committees, conference organization, journal refereeing, and stays at other research centres.
? Every career stage since the defence of the doctoral thesis will be taken into consideration
B) Maximum score: 20 points
Adequacy of the candidate?s training and experience for the contract applied for
? Applicants must have considerable experience in image processing, data programming and big data analysis.
? Experience with deep imaging data and pattern recognition are also of utmost interest.
Method of accreditation for both sections: Research statement, which includes a report on the technical and research activity carried out, accompanied by documentation that proves what was stated in it. Candidates should also indicate how they could contribute to the currently advertised project.
C) Interview. Maximum score: 20 points
Candidates who have obtained the minimum score to pass the first phase (50 points) will be called to the second assessment phase at a specific time and date. The second phase will consist in an interview that can take place either in person at the IAC headquarters or using internet communication software (e.g., skype or zoom) that allows voice and image transmission.
During the interview, the candidates will present, for a maximum of 20 minutes, their vision about the suitability of their professional profile to the position applied for and the development of their research activity if awarded the contract. The Selection Committee may then ask questions about the contents of the presentation and any other aspects that they deem relevant.
The presentation may be in either Spanish or in English.
In this exercise a score of 0 to 20 points will be awarded, of which:
Up to 10 points, correspond to the suitability of the professional profile to the position applied for.
Up to 10 points, correspond to the candidate's vision of the future and planned research activity in the context of the position applied for.
To pass this second phase a minimum score of 15 points will be necessary.
To pass the selective process, it will be necessary to obtain a minimum score of 65 points.
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