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Research engineer, greenhouse gas emissions retrieval by satellite and uncertainty quantification (24 months)

Inria -
Sophia Antipolis (06)

Soumettre votre candidature

Détails de l'emploi

CDD
À partir de 2 692 € par mois
Il y a 11 heures

Avantages

Qualifications

Français
Informatique
Niveau Doctorat
PyTorch
Anglais
Master
C
Machine learning
Python

Description complète du poste

Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Autre diplôme apprécié : PhD in Machine Learning, Data Science, Applied Mathematics, Statistics, or a closely related field is required.

Fonction : Ingénieur scientifique contractuel

A propos du centre ou de la direction fonctionnelle

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

Contexte et atouts du poste

Job environment

This position is part of a cooperation between the Maasai team and the company QAIrbon, with the goal of improving uncertainty quantification of greenhouse gas (GHG) emission predictions. Maasai (Models and Algorithms for Artificial Intelligence) is an Inria team working on machine learning and its applications. QAIrbon is a start-up founded in 2024 developing a solution to quantify worldwide industrial CO2 emissions at the facility level leveraging satellite imagery. It aims at providing a measurement-based approach to replace voluntary reporting using emission factors prone to errors and lack of reliability.

Travel and mobility

The recruited engineer will be integrated into the Maasai team at Centre Inria at Université Côte d’Azur and will interact regularly with project partners from both Inria and QAIrbon. The position involves occasional travel for collaboration meetings between partners and participation to international conferences for dissemination of results. Travel expenses are covered by the contract.

Mission confiée

We are seeking a highly skilled Machine Learning Researcher/Data Scientist to work on the retrieval of greenhouse gases (GHG) concentrations from satellite measurements [1,2,3], and uncertainties stemming from the interpolation of detected emissions in the concentration maps.

Satellite observations are hyperspectral images: two spatial dimensions (~30 meters resolution), and a spectral dimension (~5 nanometers resolution). A pixel is thus a vector of size N, the number of measured spectral channels (~100). The spectral dimension allows the identification of atmospheric species using the knowledge of their absorption spectrum.

Existing retrieval methods perform [4] the inversion of a state vector describing the atmospheric composition of a satellite pixel. This inversion requires many a priori estimated parameters and relies on a so-called Radiative Transfer Model (RTM) [5] as a forward model that simulates the interaction of light with the atmosphere and the ground before it is being measured by the satellite. The inversion is traditionally performed on a per-pixel basis and the RTM is a heavy full-physics model, so processing times can be long and expensive in an operational setting.

Two complementary approaches will be explored:

Speed up and enhance the traditional retrieval framework by using neural networks as forward models. The training of such a surrogate model would be done using synthetic data generated from an existing RTM.
Direct learning of an inverse model by a neural network to directly produce a concentration map from a satellite observation.

From these GHG concentration maps, spot emissions are detected and quantified and eventually used along other data to estimate total emissions on a continuous period of time. In this role, you will additionally apply state-of-the-art uncertainty quantification techniques—specifically Conformal Prediction [6]—to this complex, multi-stage measurement pipeline. Your mission will be to ensure our environmental data is not just highly accurate, but rigorously bounded using distribution-free confidence intervals

References

[1] C. Borger, S. Beirle, A. Butz, L. O. Scheidweiler, and T. Wagner, “High-resolution observations of NO2 and CO2 emission plumes from EnMAP satellite measurements,” Environ. Res. Lett., vol. 20, no. 4, p. 044034, Mar. 2025, doi: 10.1088/1748-9326/adc0b1.

[2] D. H. Cusworth et al., “Quantifying Global Power Plant Carbon Dioxide Emissions With Imaging Spectroscopy,” AGU Advances, vol. 2, no. 2, p. e2020AV000350, June 2021, doi: 10.1029/2020AV000350.

[3] M. Dogniaux et al., “The Adaptable 4A Inversion (5AI): description and first X CO2 retrievals from Orbiting Carbon Observatory-2 (OCO-2) observations,” Atmos. Meas. Tech., vol. 14, no. 6, pp. 4689–4706, June 2021, doi: 10.5194/amt-14-4689-2021.

[4] C. Frankenberg, U. Platt, and T. Wagner, “Iterative maximum a posteriori (IMAP)-DOAS for retrieval of strongly absorbing trace gases: Model studies for CH4 and CO2 retrieval from near infrared spectra of SCIAMACHY onboard ENVISAT,” Atmos. Chem. Phys., 2005, https://acp.copernicus.org/articles/5/9/2005/

[5] C. Rodgers, Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation, Reviews of Geophysics, vol.18, issue.7, pp.609-624, 1976, doi: 10.1029/RG014i004p00609

[6] A. Angelopoulos, R. Foygel Barber, and S. Bates, Theoretical Foundations of Conformal Prediction, Cambridge University Press, 2025, https://arxiv.org/abs/2411.11824

Principales activités

Main activities (5 maximum) :

Main activities:

Design, and implement neural architectures for surrogates of the forward model or to solve the inverse problem. Evaluate the performance of different training strategies for each task.
Read and implement research papers that tackle similar problems.
Compare the benefits and drawbacks of the two approaches (forward surrogate and inverse model).
Learn, and implement Conformal Prediction using Python. Leverage modern conformal prediction techniques, first on simple datasets then for our existing GHG emission models.
Apply uncertainty evaluation at every step of the measurement chain.

Additional activities:

Improve conformal prediction: The project is likely to foster research questions about uncertainty quantification in general. This could lead to both empirical and theoretical work, and research papers.
Work on other machine learning projects that involve Maasai and QAIrbon

Compétences

Advanced degree (Masters or Ph.D.) in Machine Learning, Data Science, Applied Mathematics, Statistics, or a closely related field is required.

Proficiency in Python and modern machine learning libraries (PyTorch, Scikit-learn) is required.

Knowledge in spectroscopy and atmospheric science (radiative transfer modelling) is a plus.

Experience with optical satellite data (multispectral, hyperspectral) is a plus.

Avantages

Subsidized meals
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Social, cultural and sports events and activities
Access to vocational training
Social security coverage

Rémunération

From 2692 € gross monthly (according to degree and experience)

Informations générales

Thème/Domaine : Optimisation, apprentissage et méthodes statistiques
Statistiques (Big data) (BAP E)
Ville : Sophia Antipolis
Centre Inria : Centre Inria d'Université Côte d'Azur
Date de prise de fonction souhaitée : 2026-10-01
Durée de contrat : 2 ans
Date limite pour postuler : 2026-07-17

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

Sécurité défense :
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :
Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

Équipe Inria : MAASAI
Recruteur :
Mattei Pierre-alexandre / [email protected]

A propos d'Inria

Inria est l’institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l’interface d’autres disciplines. L’institut fait appel à de nombreux talents dans plus d’une quarantaine de métiers différents. 900 personnels d’appui à la recherche et à l’innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eﬀorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.

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