Postdoctoral Researcher (M/F) – Late Data Fusion for Exoplanet Characterization

CNRS; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A research institute in France seeks a postdoctoral researcher to develop innovative data fusion methodologies for exoplanet characterization. The successful candidate will work collaboratively to improve the FORMOSA tool using statistical inference and machine learning. A PhD in Applied Mathematics, Computer Vision, or Data Science is required, along with strong programming skills in Python. This is a full-time position based in France, starting April 2026.

Qualifications

• PhD in Applied Mathematics, Computer Vision, or Data Science required.
• Strong programming skills in Python are essential.
• Knowledge of statistical inference methods and machine learning is crucial.

Responsabilités

• Propose innovative data fusion approaches.
• Publish results in top-tier journals.
• Coordinate and document developments within the FORMOSA code.

Connaissances

Statistical inference methods

Machine learning

Programming in Python

Experience in spectroscopy

Written communication in English

Formation

PhD in Applied Mathematics, Computer Vision, or Data Science

Outils

Python

Julia

Matlab

Description du poste

Organisation/Company CNRS Department Institut de planétologie et d'astrophysique de Grenoble Research Field Computer science Researcher Profile First Stage Researcher (R1) Country France Application Deadline 5 Dec 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Apr 2026 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

More than 5,000 exoplanets have been discovered to date. The inventory is still ongoing, but our field is now heavily investing in the characterization of the physical and chemical properties of these objects through the use of sensitive imaging cameras and spectrographs with various spectral resolutions, operating from 0.5 to 28 µm.

Our group has developed the Bayesian modeling tool FORMOSA (Petrus et al. 2023). It allows the inference of low‑resolution (R = λ/Δλ = 30) and high‑resolution (R = 100,000) spectra of exoplanets using precomputed grids of models. The code can estimate global properties (effective temperature, pressure–temperature profiles) as well as atmospheric compositions, vertical cloud distributions, and particle sizes.

The nature of the data we interpret with FORMOSA is becoming increasingly complex as new instruments come online. Data formats can be heterogeneous, datasets unbalanced, and they may cover overlapping wavelength ranges with different signal‑to‑noise ratios and spectral resolutions. Moreover, the atmospheric models we use remain imperfect and can exhibit systematic deviations that bias the characterization process.

Data fusion is a branch of data science (see Lahat et al. 2015) that aims to combine datasets corresponding to the same phenomenon for decision‑making or to obtain tighter constraints on a model (e.g., in weather forecasting). In this context, jointly considering data acquired by different instruments on the same objects has the potential to yield more robust and reliable estimates. Examples of data fusion in astrophysics have already been demonstrated in the context of disentangling galaxy spectra from different modalities (see the ODHIN method in Bacon et al., 2023), based on simplified assumptions. Overcoming the limitations inherent to these methods and adapting them to exoplanet imaging modalities represents a new challenge.

The postdoctoral researcher will investigate how data fusion techniques can be incorporated into FORMOSA to address the challenges described above. They will propose new methodologies based on statistical inference and machine learning applied to data fusion and adapt them to the field of exoplanet characterization. They will develop and maintain the FORMOSA code in coordination with the team of students working on its development in France, and will apply the new methodology to exoplanet characterization as a proof of concept.

Responsibilities

• Propose and adapt innovative approaches based on data fusion to address the challenges mentioned above.
• Publish the results in top‑tier (A‑ranked) journals.
• Communicate the results to both the data science and astronomy communities.
• Coordinate, maintain, and document developments within the FORMOSA code.

The successful candidate will work within the research groups of Mickaël Bonnefoy, Mauro Dalla Mura, and Florent Chatelain at the Institut de Planétologie et d'Astrophysique de Grenoble (IPAG) and GIPSA‑Lab (Grenoble Images Parole Signal Automatique), both located on the main campus in Grenoble. These laboratories provide a rich collaborative environment at the intersection of astrophysics and signal processing. The work will be carried out as part of the ANR MIRAGES project, hosted by the LAM (Marseille), LESIA (Paris), IPAG (Grenoble), and Lagrange (Nice) laboratories, and coordinated by A. Vigan (LAM). MIRAGES focuses on the characterization of exoplanets and aims to exploit data from the newly commissioned HiRISE instrument.

Qualifications

• PhD in Applied Mathematics, Computer Vision, or Data Science.
• Knowledge of statistical inference methods and machine learning.
• Experience in spectroscopy and imaging is an asset.
• Strong programming skills in Python; familiarity with Julia and Matlab is appreciated.
• Excellent written communication skills in English.