Université d'Orléans
6 days ago
Postdoctoral Researcher in Data Science & Machine Learning for Financial Fraud Detection Université d'Orléans in France
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Nov 12, 2026
Country
France
University
Université d'Orléans
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About this position
Postdoctoral Researcher in Data Science & Machine Learning for Financial Fraud Detection at Université d'Orléans (France).
This postdoctoral position is hosted by the Laboratoire d'Économie d'Orléans (LEO) and focuses on developing interpretable and operational machine learning and deep learning methods for fraud detection across insurance, social contributions, and banking. The project addresses major practical challenges in detecting anomalous behaviour, suspicious networks, and emerging fraud patterns while controlling false positives and preserving model interpretability.
The research agenda includes modelling under class imbalance, (semi-)supervised and cost-sensitive learning, network and graph methods to uncover coordinated schemes, explainable AI (XAI), robust modelling for regulated environments, and temporal/adaptive strategies as fraud techniques evolve. The work will use large, heterogeneous, and often sensitive proprietary datasets, with emphasis on data confidentiality, responsible use, and reproducibility.
The successful candidate will join a multidisciplinary research environment with access to high-performance computing, proprietary fraud datasets, mentoring from senior researchers, and opportunities for scientific publication and international conference participation. The role includes collaboration with academic and institutional partners, and validation of models with industry stakeholders.
Eligibility highlights: candidates should hold a PhD in Data Science, Machine Learning, Econometrics, Finance, Statistics, or a related field, or be close to defending their doctoral thesis. Strong technical skills in ML/DL, Python/R, feature engineering, large-scale data handling, and XAI are expected. Experience with graph/network analysis is an asset. Excellent English, teamwork, independent research ability, and careful handling of confidential data are important; French and prior experience with insurance, banking, or social protection data are advantageous.
Application materials: CV with publications, cover letter (maximum 2 pages), and one representative research paper. Deadline: 12 November 2026. Start date: 4 January 2027.
Funding details
Available
What's required
A PhD in Data Science, Machine Learning, Econometrics, Finance, Statistics, or a related field is required, or the candidate must expect to defend the doctoral thesis in the near future. Candidates should have advanced expertise in machine learning and deep learning, be proficient in Python, R, or equivalent programming tools, and have experience with large-scale datasets and feature engineering. Familiarity with explainable AI methods is expected, and experience with network or graph analysis is an asset. Applicants should be able to conduct independent research, have excellent written and spoken English, work collaboratively, and handle confidential data appropriately. Strong interest in fraud detection or financial risk analysis is important; experience with insurance, banking, or social protection data and proficiency in French are considered assets.
How to apply
Prepare a CV including a list of publications, a cover letter describing your research interests and fit, and one representative research paper. Apply through the Jobaffinity portal using the provided application link.
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