Post-Doctoral Research Visit F/M Characterization of motion anomalies in videos: Application to the detection of AI-generated videos

Post-Doctoral Research Visit F/M Characterization of motion anomalies in videos: Application to the detection of AI-generated videos Download job offer in PDF format Contract type : Fixed-term contract Level of qualifications required : PhD or equivalent Other valued qualifications : PhD thesis Fonction : Post-Doctoral Research Visit About the research centre or Inria department The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc. Context Funding: Funding for this postdoc position has not yet been secured. The selection of a postdoc candidate is a prerequisite. This is because the funding sources being sought require that the application be submitted by the postdoc fellow. The duration of the postdoc position may range from 12 to 24 months, depending on the funding secured. Assignment Subject: A video is more than just a succession of images because it incorporates the fundamental temporal dimension of motion. Motion indeed carries intrinsic information in videos. This characteristic is essential to the analysis. Additionally, motion properties become more explicit over time. Motion is fully represented by the velocity field computed between two images at every time instant of the video. In the long term, the consecutive velocity fields can be viewed as a multivariate time series. The first objective of the postdoc is to create parsimonious time series through learning that can adequately represent motion content and are semantically suitable for generic tasks such as detection, recognition, and classification. More specifically, we will explore anomaly detection in videos, a challenge shared by many applications, albeit in various forms. We will focus on the characterization of motion anomalies based on learned time series. Motion anomalies can manifest themselves in three main ways: deviations from normal behavior or context (e.g., a vehicle driving the wrong way on a highway), sudden divergences (e.g., a vehicle leaving the road, panic in a crowd), or non-natural motion (e.g., presumably in AI-generated videos). This last category will lead us to address the detection of AI-generated videos, whose rapid rise and growing ability to mimic reality raise major societal and economic issues. While the detection of AI-generated still images has been the subject of much research and even challenges, there are still relatively few methods designed specifically for videos. Methods developed for detecting AI-generated images are not effective for videos because they fail to recognize an essential video characteristic: its temporal dimension. This postdoc will build on our recent work, particularly on salient trajectory detection, long-term unsupervised motion segmentation, and automatic detection of AI-generated content. Key words: Motion in image sequences, time series, learning, anomaly characterization, detection of AI-generated videos References - L. Maczyta, P. Bouthemy, and O. Le Meur. Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder, IEEE Trans. on Circuits and Systems for Video Technology, 32(4):1724 – 1738, April 2022. - E. Meunier and P. Bouthemy. Segmenting the motion components of a video: A long-term unsupervised model, IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(1):500-511, January 2026. - G. Charbel, N. Kindji, E. Fromont, L. M. Rojas-Barahona, and T. Urvoy. Robust detection of synthetic tabular data under schema variability, 40th Annual AAAI Conf. on Artificial Intelligence (AAAI'2026), Singapore, January 2026. 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 (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 Remuneration Monthly gross salary from 2 788 euros. Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Optimization, machine learning and statistical methods Statistics (Big data) (BAP E) • Town/city : Rennes • Inria Center : Centre Inria de l'Université de Rennes • Starting date : 2026-10-01 • Duration of contract : 1 year, 6 months • Deadline to apply : 2026-09-30 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 Please submit online : your resume, cover letter and letters of recommendation eventually 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 : MALT • Recruiter : Bouthemy Patrick / patrick.bouthemy@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.