Post-Doctoral Research Visit F/M Learning imaging systems and solving inverse problems from unpaired data

Post-Doctoral Research Visit F/M Learning imaging systems and solving inverse problems from unpaired data Download job offer in PDF format Contract type : Fixed-term contract Renewable contract : Yes Level of qualifications required : PhD or equivalent Fonction : Post-Doctoral Research Visit About the research centre or Inria department The Centre Inria de l’Université de Grenoble groups together almost 450 people in 26 research teams and 9 research support departments. Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area. The Centre Inria de l’Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world. Context The post-doctoral researcher will be part of the Thoth team, which is specialized in computer vision and machine learning, and will be supervised by Julien Mairal. Thoth is one of the most recognized European research teams in machine learning and computer vision, is part of the Laboratoire Jean Kuntzmann at Université Grenoble Alpes, participates in several European projects (including ERCs), and has many international academic and industrial collaborations. The post-doc will join an active group of PhD students and post-docs working on generative modeling, optimization and inverse problems. Location: The post-doc will work at the Inria centre at Université Grenoble Alpes. Grenoble is a lively city which hosts many foreign students and researchers. Located in the heart of the French Alps, its direct surroundings offer great outdoor recreation including skiing, cycling, and hiking. Grenoble is well-connected to Lyon and Geneva airports, and Paris can be reached from Grenoble in 3h by train. Assignment The post-doc is expected to conduct high-quality research leading to publications in top-tier venues in machine learning, computer vision or signal processing, and will also have the oppor- tunity to interact with and co-supervise PhD students of the team. Research Context: Inverse problems are ubiquitous in scientific and engineering domains that involve image acquisition, from computational photography to microscopy, astrophysics and medical imaging. The quantity of interest is observed through a forward process (blur, noise, subsampling, sensor effects) and must be recovered from these degraded measurements. Modern solvers, such as plug-and-play algorithms or methods based on diffusion and flow- based generative models, combine a model of the acquisition process with a powerful learned prior on clean signals, and achieve impressive results when the forward operator is known. In practice, however, the forward operator is rarely known precisely. Calibrating a real imaging system typically requires dedicated hardware or pairs of clean and degraded images, which are expensive or even impossible to acquire, for instance for a real camera lens. A recent line of work in the Thoth team has shown that the forward operator can instead be learned from unpaired data, by matching distributions both on the measurement side and on the reconstruction side, through a differentiable plug-and-play solver parameterized by the operator being learned. This opens the door to imaging systems that calibrate themselves from data, and to reconstruction methods that remain accurate on real-world acquisitions. Main activities Objectives: The goal of the post-doc is to develop principled and scalable methods for learning imaging systems and solving inverse problems when supervision is scarce or unavailable. The research directions are deliberately open and will be shaped together with the candidate according to their interests and expertise. They include, for instance: -ˆ extending unpaired operator learning to richer forward models (spatially varying, nonlinear, or with unknown noise statistics) and to new acquisition modalities; -ˆ jointly learning the forward operator and the reconstruction method, possibly leveraging modern generative models (diffusion models, flow matching) as priors; -ˆ understanding the theoretical aspects of the problem, such as identifiability of the operator from unpaired data and the interplay between calibration and reconstruction quality; -ˆ designing models that can be trained across many devices or degradations and adapt quickly to new ones; ˆ- applying these methods to real-world imaging problems, in computational photography or in scientific imaging. Skills Desired Skills: The candidate should hold a PhD in machine learning, computer vision, signal or image processing, applied mathematics, or a related field, and have a strong publication record. A good background in mathematics (optimization, probability, statistics) and strong programming skills in Python and PyTorch are expected. Prior experience with inverse problems, generative models or optimal transport will be appreciated. Benefits package • 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 (90 days / year) and flexible organization of working hours • Social, cultural and sports events and activities • Access to vocational training • Social security coverage under conditions Remuneration 2927 € gross salary / month Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Vision, perception and multimedia interpretation Statistics (Big data) (BAP E) • Town/city : Montbonnot • Inria Center : Centre Inria de l'Université Grenoble Alpes • Starting date : 2027-01-01 • Duration of contract : 1 year, 6 months • Deadline to apply : 2026-12-05 Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. Instruction to apply Applications must be submitted online via the Inria website. Processing of applications submitted via other channels is not guaranteed. Information for Applicants: Please note that all public officials are required to comply with ethical rights and obligations: https://www.fonction-publique.gouv.fr/etre-agent-public/mes-droits-et-obligations Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Contacts • Inria Team : THOTH • Recruiter : Mairal Julien / Julien.Mairal@inria.fr About Inria Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.