Internship: Statistics methods for neuroimaging data reuse in the presence of sample overlap

Internship: Statistics methods for neuroimaging data reuse in the presence of sample overlap Download job offer in PDF format Level of qualifications required : Master's or equivalent Other valued qualifications : Stage de Master 2 Fonction : Internship Research About the research centre or Inria department The Inria center at the University of Rennes is one of eight Inria centers and has more than thirty research teams. The Inria center is a major and recognized player in the field of digital sciences. It is at the heart of a rich ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes. Assignment 10 years ago, a series of publications pointed to the difficulty of reproducing scientific findings. This reproducibility crisis was a wake-up call for scientific communities to rethink how we practise research, and an important driver towards greater transparency and robust results. In the field of brain imaging (or neuroimaging), this momentum highlighted the need to increase sample sizes and gave an impetus to data sharing. Several large datasets have emerged with the goal of sharing thematic data such as the ABCD dataset for brain development. Neuroimaging has thus moved from results from small studies (N < 30 participants) to large-scale analyses (N > 1000). Data sharing has been identified as one of the keys to maximize those two types of variability while making the best use of the available data. Secondary use of neuroimaging data involves combining multiple datasets and re-purposing them to answer a new research question. In this process – in departure from primary use – researchers typically combine pseudonymized datasets in which only minimal information is retained about each participant. While this is an important feature to protect participant’s privacy, in practice it also means that data from the same participant may be included twice without the researcher being aware of it. This induces a dependence in the input data, a phenomenon known as sample overlap. The goal of this internship will be to measure, quantify and mitigate sample overlap in neuroimaging data reuse. Specifically, the successful applicant will focus on task functional Magnetic Resonance Imaging (task-fMRI) an in vivo imaging technique for the study of brain function. This work will be supervised by Dr. Camille Maumet and is part of the ANR JCJC VICUNA. Main activities To reach this goal you will : • Familiarise yourself with OpenNeuro and Neurovault, two repositories for brain data sharing. • Learn about neuroimaging statistics with dependant data in Magnetic Resonance Imaging (see for example (Germani et al. 2025; Lefort-Besnard et al. 2025)); • Read scientific papers on sample overlap (Bom and Rachinger 2020), neuroimaging data analysis (Mumford and Nichols 2009) • Develop code to extract information from OpenNeuro and NeuroVault, build simulations Références • Bom, Pedro R. D., and Heiko Rachinger. 2020. “A Generalized-Weights Solution to Sample Overlap in Meta-Analysis.” Research Synthesis Methods 11 (6): 812–32. https://doi.org/10.1002/jrsm.1441. • Germani, Elodie, Xavier Rolland, Pierre Maurel, and Camille Maumet. 2025. “On the Validity of fMRI Mega-Analyses Using Data Processed with Different Pipelines.” Imaging Neuroscience 3 (April): imag_a_00522. https://doi.org/10.1162/imag_a_00522. • Lefort-Besnard, Jeremy, Thomas E. Nichols, and Camille Maumet. 2025. “Statistical Inference for Same Data Meta-Analysis in Neuroimaging Multiverse Analyzes.” Imaging Neuroscience 3 (March): imag_a_00513. https://doi.org/10.1162/imag_a_00513. • Mumford, Jeanette A., and Thomas Nichols. 2009. “Simple Group fMRI Modeling and Inference.” NeuroImage 47 (4): 1469–75. https://doi.org/10.1016/j.neuroimage.2009.05.034. Skills Required skills • Masters in statistics or data science • Strong interest in medical imaging • Good communication skills • Good programming skills • Excellent understanding of English Benefits package • Subsidized meals • Social, cultural and sports events and activities Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Computational Neuroscience and Medicine Biologie et santé, Sciences de la vie et de la terre (BAP A) • Town/city : Rennes • Inria Center : Centre Inria de l'Université de Rennes • Starting date : 2027-02-01 • Duration of contract : 6 months • Deadline to apply : 2026-12-13 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 : EMPENN • Recruiter : Maumet Camille / camille.maumet@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.