Post-Doctoral Research Visit F/M Theoretical analysis of generative models
Level of qualifications required : PhD or equivalent
Fonction : Post-Doctoral Research Visit
Context
The position will be in the framework of the ERC Starting Grant DYNASTY (Dynamics-Aware Theory of Deep Learning).
The position might include traveling to conferences for paper presentation. Travel expenses will be covered within the limits of the scale in force.
Assignment
The rapid success of diffusion models [1, 2, 3], which have achieved state-of-the-art performance across diverse domains, motivates the need for a theoretical understanding of the mechanisms underpinning their strong capabilities. The unique structure of these models, as well as recent evidence [4, 5] indicating they forgo the advantageous properties of benign overfitting, suggests that diffusion models are a fundamental divergence from traditional deep learning paradigms. This suggests that existing generalisation theories are insufficient and highlights the need for a bespoke, algorithm-dependent framework to capture the phenomena present in these models.
We are seeking a postdoctoral researcher to develop theoretical frameworks for analysing generalisation and memorisation in diffusion models. The project's central goal is to move beyond algorithm-independent bounds and develop rigorous theory that unpacks the generalisation properties inherent to the training and sampling processes. A key component of this analysis will be to precisely characterise the mechanisms driving memorisation. This theoretical work will serve as the foundation for developing well-founded algorithms that target the task of preventing data copying (e.g. [6, 7]) as well as the problem of data attribution (e.g. [8, 9]), allowing for the precise measurement of individual training examples' influence on model outputs.
[1] Sohl-Dickstein, Jascha, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” ICML.
[2] Ho,Jonathan, Ajay Jain, and Pieter Abbeel. 2020. “Denoising Diffusion Probabilistic Models.” NeurIPS.
[3] Song, Yang, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021. “Score-Based Generative Modeling through Stochastic Differential Equations.” ICLR.
[4] Pidstrigach, Jakiw. 2022. “Score-Based Generative Models Detect Manifolds.” NeurIPS.
[5] Dupuis, Benjamin, Dario Shariatian, Maxime Haddouche, Alain Durmus, and Umut Simsekli. 2025. “Algorithm- and Data-Dependent Generalization Bounds for Score-Based Generative Models.” arXiv [Stat.ML].
[6] Vyas, Nikhil, Sham M. Kakade, and Boaz Barak. 2023. “On Provable Copyright Protection for Generative Models.” ICML.
[7] Alberti, Silas, Kenan Hasanaliyev, Manav Shah, and Stefano Ermon. 2025. “Data Unlearning in Diffusion Models.” ICLR.
[8] Mlodozeniec, Bruno Kacper, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, and Richard E. Turner. 2025. “Influence Functions for Scalable Data Attribution in Diffusion Models.” ICLR.[9] Zheng, Xiaosen, Tianyu Pang, Chao Du, Jing Jiang, and Min Lin. 2024. “Intriguing Properties of Data Attribution on Diffusion Models.” ICLR.
Main activities
Main activities :
Conduct theoretical research
Conduct experiments for empirical verification
Write scientific articles
Disseminate the scientific work in appropriate venues.
Skills
Technical skills and level required :
Languages : High-level of professional/academic English
Coding skills : Good level of coding in Python and related deep learning libraries
Benefits package

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 and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Social, cultural and sports events and activities
Theme/Domain :Optimization, machine learning and statistical methodsStatistics (Big data)(BAP E)

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Instruction to apply
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.
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.
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