Researcher in numerical modeling of space instruments for the Euclid mission M/F
- Marseille
- CDD
- Temps-plein
The NISP instrument is a slitless spectrometer consisting of the largest infrared focal plane sent into space to date. With its wide field of view, NISP captures the spectrogram of all sources within its observational field, covering an angular surface equivalent to the apparent size of the Moon. The instrument's extensive field of view, combined with numerous sources, poses a challenge for conventional simulators to realistically model the images necessary for cosmological analyses within limited time frames.
Advancements in generative Machine Learning methods offer new application prospects. The ANR DISPERS project (2022-2026, ) aims to develop slitless spectroscopy simulation tools based on deep learning for the Euclid space mission and future large cosmological surveys. This initiative opens new opportunities for planning cosmological surveys, analyzing instrumental error sources, and applying methods used for spectrum decontamination.
The selected candidate will play a central role in developing an innovative simulation tool aimed at transforming the way large galaxy surveys are analyzed. The simulator's primary objective is to realistically model the response of the NISP instrument, leveraging recent machine learning algorithms, advanced statistical techniques, and numerical optimization formalisms. The simulator aims to create precise simulations capturing the subtleties of both instrumental and astronomical phenomena, contributing to enhancing our understanding of Euclid data and future surveys.The postdoctoral researcher will specifically contribute to various aspects of the project:
· Develop and implement machine learning algorithms for modeling instrument responses, in collaboration with team members.
· Apply advanced statistical and machine learning techniques to analyze mission data.
· Contribute to the design and execution of experiments, simulations, and data analyses.
· Participate in the validation and calibration of simulation tools using Euclid mission data.
· Contribute to research publications, conference presentations, and scientific workshops to disseminate research findings.
· Collaborate with doctoral students, researchers, and engineers in the project to promote a collaborative and innovative research environment.Located in the heart of the Calanques National Park, on the Luminy campus, CPPM is a research laboratory jointly affiliated with CNRS (National Center for Scientific Research) and Aix-Marseille University. It comprises approximately 180 researchers, engineers, and doctoral students. The laboratory conducts research spanning from particle physics to astroparticle physics and cosmology, with strong technological expertise in electronics, mechanics, instrumentation, and computer science. This expertise enables the design and construction of cutting-edge detection systems, often required to operate under extreme conditions: in the depths of the sea, in space, or underground. Most of our research is carried out within leading international scientific collaborations, and our contributions are recognized worldwide. CPPM is committed to conducting ethical research and promoting diversity and inclusion in the workplace. The laboratory provides administrative and logistical support to newcomers, especially doctoral students. (For more details, visit: CPPM Website ).
The Renoir team at CPPM consists of 10 permanent researchers, 4 engineers, 3 postdoctoral researchers, and 7 doctoral students. The position is funded by the ANR DISPERS project for a duration of exactly 2 years.
Travel may be required as part of the planned activities.RequirementsResearch Field Physics Education Level PhD or equivalentLanguages FRENCH Level BasicResearch Field Physics Years of Research Experience NoneAdditional InformationEligibility criteria· Ph.D. in astrophysics, computer science, or a related field, with expertise in machine learning or computational astrophysics.
- More than two years' professional experience.
· Strong experience in machine learning, statistical modeling, and data analysis, with applications in astrophysics or related fields.
· Skills in the Python programming language and libraries such as TensorFlow, PyTorch, JAX, or similar, for implementing machine learning algorithms and GPU computing.
· Experience in instrument modeling, image simulations, or related fields would be a significant advantage.
· Practical experience with Physics-Informed Neural Networks (PINNs) and/or transfer learning methods would also be a major asset.
· Soft skills enabling collaborative daily work, coupled with a high degree of autonomy in tasks.
· Excellent analytical skills and problem-solving abilities.
· Outstanding communication skills, including the ability to present research results clearly and concisely.Website for additional job detailsWork Location(s)Number of offers available 1 Company/Institute Centre de Physique des Particules de Marseille Country France City MARSEILLE 09 GeofieldWhere to apply WebsiteContact CityMARSEILLE 09 WebsiteSTATUS: EXPIRED
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