Job Information
- Organisation/Company: Inria, the French national research institute for the digital sciences
- Research Field: Computer science
- Researcher Profile: First Stage Researcher (R1)
- Application Deadline: 31 Dec 2026 - 00:00 (UTC)
- Country: France
- Type of Contract: Temporary
- Job Status: Full-time
- Hours Per Week: 38.5
- Offer Starting Date: 4 Jan 2027
- Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme
- Reference Number: [contact details available in the full listing]
- Is the Job related to staff position within a Research Infrastructure?: No
Offer Description
World Models are a family of models that offer an alternative to classical approaches such as vision models or large language models. Instead of mapping inputs to outputs, they learn an internal representation of a “world” and predict how that world evolves over time and in response to actions. This makes them natural candidates for prediction, simulation and planning.
These models are known to be hard to train. Many recent approaches build on the Joint-Embedding Predictive Architecture (JEPA) [1–4], which predicts in a learned representation space rather than reconstructing raw inputs. Within this line of work, LeWorldModel (LeWM) [5, 6] was recently proposed. It trains end to end in a stable way and has shown good properties for training.
Objectives
The LeWM methodology is general, but so far it has mainly been applied to image inputs. This internship will explore how it can be adapted to networking problems. Wireless networks are a good fit for this approach: their state, such as topology, connectivity and traffic, changes over time and in response to decisions. Prior work in the team has studied related problems [7].
Work plan
- Study the World Model and JEPA literature, then reproduce and test the existing LeWM model.
- Choose a suitable networking application and an input representation, based on feasibility and relevance.
- Adapt and retrain the model on this application, possibly through a sequence of tasks of increasing complexity.
- Evaluate the results experimentally and analyze both the predictions and the learned representations.
Candidate profile
- Solid background in machine learning and deep learning, with practical experience in Python and PyTorch
- Interest in networking (wireless networks are a plus, but not required)
- Familiarity with self-supervised learning, representation learning or reinforcement learning is a plus
References
- Y. LeCun, “A Path Towards Autonomous Machine Intelligence,” OpenReview, 2022.
- M. Assran et al., “Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture” (I-JEPA), CVPR 2023.
- A. Bardes et al., “Revisiting Feature Prediction for Learning Visual Representations from Video” (V-JEPA), TMLR 2024.
- M. Assran et al., “V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning,” arXiv:[contact details available in the full listing], 2025.
- R. Balestriero and Y. LeCun, “LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics,” arXiv:[contact details available in the full listing], 2025.
- L. Maes, Q. Le Lidec, D. Scieur, Y. LeCun, and R. Balestriero, “LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels,” arXiv:[contact details available in the full listing], Mar. 2026.
- F. Marcoccia, V. Fagoo, G. M. de Saint Julien, C. Adjih, T. Watteyne, and P. Mühlethaler, “NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies,” ICML 2026.
Where to apply
Website: https://jobs.inria.fr/public/classic/en/offres/[contact details available in the full listing]
Requirements
- Languages: FRENCH — Level: Basic
- Languages: ENGLISH — Level: Good
Additional Information
Benefits
- 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 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
- Social security coverage
Website for additional job details: https://jobs.inria.fr/public/classic/en/offres/[contact details available in the full listing]
Work Location(s)
- Number of offers available: 1
- Company/Institute: Inria
- Country: France
- City: Palaiseau
Contact
- City: LE CHESNAY CEDEX
- Website: http://www.inria.fr
- Street: Domaine de Voluceau - Rocquencourt
- Postal Code: 78153
