TELECOM ParisTech
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Post-doctoral fellow in Statistical Learning and Generative AI (12-month contract) Télécom Paris in France
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Oct 14, 2026
Country
France
University
TELECOM ParisTech

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About this position
Télécom Paris is offering a 12-month postdoctoral fellowship in Statistical Learning and Generative AI within the Image, Data and Signal (IDS) Department and the LTCI lab. The position sits in the S2A group (Signal, Statistics and Machine Learning), a research environment focused on AI and machine learning across multiple data modalities.
The postdoc will contribute to a research initiative on uncertainty quantification for Euclidean and non-Euclidean data, with a particular focus on the calibration of generative models. The project is motivated by the need for generative AI systems that can provide meaningful uncertainty measures about targeted properties at inference time, especially in high-stakes contexts such as health-related applications. The work will explore recent calibration tools for multivariate data generation and may extend to graph data and other non-Euclidean settings.
The role is research-oriented and also includes supervision and tutoring responsibilities, together with broader contributions to the visibility of Télécom Paris, Institut Mines-Télécom, and Institut Polytechnique de Paris. The successful candidate will join a strong research environment at one of France’s leading engineering schools, located in Palaiseau, France, near Paris.
Eligibility highlights: applicants must hold a PhD or equivalent in statistical learning or a closely related discipline. Strong expertise in statistical learning, generative models, and uncertainty quantification is required. English proficiency is essential. Knowledge of reproducing kernel Hilbert spaces and optimal transport would be an advantage.
Appointment and funding: this is a temporary full-time postdoctoral contract lasting 12 months, with a gross salary of 36,000 € per year. The position is not funded through an EU research programme.
Deadline: applications close on 14 October 2026.
Application: apply through the recruitment website link provided in the advert. The posting directs candidates to the IMT Recruitee portal for submission.
Funding details
Available
What's required
PhD or equivalent degree in statistical learning or a closely related field. Strong command of statistical learning, generative models, and uncertainty quantification methods. Proficiency in English is essential. Knowledge of reproducing kernel Hilbert spaces and optimal transport would be an asset. Candidate should also have strong interpersonal, pedagogical, analytical, summarization, and teamwork skills.
How to apply
Apply via the recruitment website link provided in the posting. Use the main application page on the IMT Recruitee portal and follow the instructions there. Submit before 2026-10-14.
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