Internship Research / World Models for Networking

Internship Research / World Models for Networking Download job offer in PDF format Contract type : Internship agreement Level of qualifications required : A levels + 2 years of higher education or equivalent Fonction : Internship Research About the research centre or Inria department Created in 2008, the Inria Saclay Center is located at the heart of the Paris-Saclay scientific and technological excellence cluster, which alone accounts for 15% of French research. Serving the development of the Université Paris-Saclay and the Institut Polytechnique de Paris, the Inria Saclay center employs 80 people in research support services and 500 scientists of 54 nationalities. Benefiting from continuous growth, the center now has a total of 42 project-teams and two in the process of being created, including 21 jointly with the Institut Polytechnique de Paris, 16 with the Université Paris-Saclay, as well as 7 Inria EPs, including one in collaboration with Onera and one with the Pôle Universitaire Centre Val de Loire. These research teams are spread over more than ten sites. Assignment 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 [1] Y. LeCun, “A Path Towards Autonomous Machine Intelligence,” OpenReview, 2022. [2] M. Assran et al., “Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture” (I-JEPA), CVPR 2023. [3] A. Bardes et al., “Revisiting Feature Prediction for Learning Visual Representations from Video” (V-JEPA), TMLR 2024. [4] M. Assran et al., “V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning,” arXiv:2506.09985, 2025. [5] R. Balestriero and Y. LeCun, “LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics,” arXiv:2511.08544, 2025. [6] L. Maes, Q. Le Lidec, D. Scieur, Y. LeCun, and R. Balestriero, “LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels,” arXiv:2603.19312, Mar. 2026. [7] 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. Benefits package • 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 Remuneration Gratification Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Networks and Telecommunications System & Networks (BAP E) • Town/city : Palaiseau • Inria Center : Centre Inria de Saclay • Starting date : 2027-01-04 • Duration of contract : 2 months • Deadline to apply : 2026-12-31 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 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 : TRIBE • Recruiter : Adjih Cédric / Cedric.Adjih@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.