PhD Student (M/F): Automatic detection of the HI gas line in massive data from pre-Square Kilom[...]

European Commission; France; Sur place; EUR 35 000 - 50 000; Plein temps

Résumé du poste

A leading research institution in Strasbourg is seeking a Researcher to contribute to astronomy research, focusing on the analysis of vast datasets. Candidates should have a Master's in Astronomy and significant experience with Machine Learning techniques. This position will involve developing digital tools for data processing and participation in international science projects leveraging advanced telescopes. The role is full-time and offers an exciting opportunity to advance in a cutting-edge research environment.

Qualifications

• Master's degree in Astronomy and Astrophysics is mandatory.
• Strong experience in Machine Learning techniques preferred.

Responsabilités

• Participate in the exploitation of SKA precursor radio telescopes.
• Develop new digital tools for detecting HI emission line.
• Adapt CIANNA software for massive data analysis.

Connaissances

Machine Learning techniques

Formation

Master in Astronomy and Astrophysics

Outils

CIANNA software

Description du poste

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Organisation/Company CNRS Department Observatoire astronomique de Strasbourg Research Field Physics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 13 Aug 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The recruited person will be assigned to the Strasbourg Astronomical Observatory within the Galaxies, High Energies, and Cosmology team (OBAS/GALHECOS) and will work under the direction of L. Chemin. The Astronomical Observatory is a joint research unit of the CNRS and the University of Strasbourg, located at the heart of the city's historic campus. It comprises approximately 100 people, including around 30 permanent scientists whose main research areas are galaxy physics, high-energy astrophysics, and data science for astronomy. The recruited person will also collaborate with regular project contributors: D. Cornu, a researcher and developer at CIANNA (Paris Observatory), and S. Blyth (PI of the observational survey with MeerKAT, University of Cape Town, South Africa).
The project has its own computational resources for development and analysis, including GPU servers. It also has access to national and international computational facilities.

The thesis is part of the activities to be carried out on behalf of the Junior Professor Chair "Massive data in the era of the Square Kilometre Array (SKA)" at the Strasbourg Astronomical Observatory. One of the scientific objectives proposed by the chair is to participate in the exploitation of SKA precursor radio telescopes, such as MeerKAT located in South Africa and ASKAP in Australia. These collect massive interferometric data for several observational and preparatory surveys for SKA, which aim to track the evolution with cosmic age of the fundamental line of neutral hydrogen gas at 21 cm in tens of thousands of galaxies. The main objective of the thesis project is to participate in the exploitation of massive MeerKAT/ASKAP data. In particular, the recruited person will measure the integrated and resolved properties of the HI gas in galaxies (gas mass and gas mass fraction, width of HI profiles, kinematics, and distribution of gas within and around galaxies, etc.) in order to constrain the evolution of the structure and dynamics of the HI gas in galaxies, via e.g., the Tully-Fisher relation, as well asf the distribution of baryonic and dark matter in galaxies. An immediate objective of the thesis will be to develop new digital tools for the detection of the HI emission line. Indeed, the classical tools for line and source detection are not fully suitable for massive data as they require recurrent visual and manual interactions for each object. An important part of the thesis will therefore focus on the development of more efficient tools to automate the detection and characterization of sources. This will be done by adapting the CIANNA software (Convolutional Interactive Artificial Neural Networks by/for Astrophysicists) to the various massive data cubes of ASKAP/MeerKAT, and by preparing galaxy samples to train, test, and validate the models built by CIANNA.

A Master in Astronomy and Astrophysics is mandatory. Preference will be for candidates with a strong experience in Machine Learning techniques.