Canon Research Centre France S.A.S.

Internship proposals

Internships are proposed in the scope of CRF corporate social responsibility (CSR). The sole aim of internships from CRF is to contribute to the education of interns who will benefit from the expertise of CRF researchers. Each year, CRF is proposing several internships, so do not hesitate to regularly visit this page.

Send your application to jobs@crf.canon.fr

AI-based Sensing and Communication in 6G

Internship duration: 5/6 months from February 2026

This internship offers a new challenge to participate to the evolution of the connecting world through the 6G mobile technology.

Internship subject

With the advent of Sixth Generation (6G) networks, the integration of Artificial Intelligence (AI) into network architectures has given rise to the concept of AI-native networks. Simultaneously, Integrated Sensing and Communication (ISAC) is emerging as a basis technology for next-generation 6G systems.
Therefore, the purpose of the internship is to explore, build and evaluate an AI-based vision-RF multimodal sensing application for 6G network optimization (e.g. Beam Prediction, Blockage Prediction, Handover, Vision Object Detection/Classification…) considering the future 6G capability for situational awareness (i.e. real-time identification, classification, and localization).

Mission

Therefore, your main work will be to provide:

Academic background

You apply for a Master 2 diploma or an engineering degree in the field of AI/ML Engineering and/or Wireless communications. You are curious, open-minded, passionate about new technologies and have real interpersonal skills to integrate an innovative and multicultural environment.

Specific knowledge

[01] Cheng, X., Zhang, H., Zhang, J., Gao, S., Li, S., Huang, Z., ... & Yang, L. (2023). “Intelligent multi-modal sensing-communication integration: Synesthesia of machines.” IEEE Communications Surveys & Tutorials, 26(1), 258-301
[02] Ahmed Alkhateeb, Gouranga Charan, Tawfik Osman, Andrew Hredzak, João Morais, Umut Demirhan, Nikhil Srinivas, “DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset”
[03] D. Scazzoli, F. Linsalata, D. Tagliaferri, M. Mizmizi, D. Badini, M. Magarini, and U. Spagnolini, “Experimental Demonstration of ISAC Waveform Design Exploring Out-of-Band Emission,” IEEE International Conference on Communications Workshops (ICC Workshops), 2023

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Representation of 3D perception / Dynamic Digital Twin

Internship duration: 4/6 months from February 2026

Internship subject

Unlike static 3D models, Dynamic Digital Twin models represent live data to analyse trends and to predict future behaviours, making them an essential tool for decision.
For 3D modelling, two popular file formats are glTF (gl Transmission Format) and USD (Universal Scene Description). glTF, an open standard developed by the Khronos Group, is designed for the efficient transmission and loading of 3D scenes and models. glTF files are compact and can be easily integrated into various platforms and tools, including game engines, AR/VR applications, and web browsers.
On the other hand, USD, an open-source file format created by Pixar, focuses on the interchange of complex 3D scenes composed from many elemental asserts. USD files are highly scalable and can accommodate the intricate details and interconnections within large-scale 3D projects.
The proposal and realization of an adapted workflow for digital twins in existing industrial environments is under study at Canon CRF, with the possibility to enhance the visual representation of the digital twin using captured real-world data (using for example a video-surveillance camera).

Mission

The goal of the internship is to build a digital twin framework using NVIDIA Omniverse SDKs and OpenUSD. The static model described using OpenUSD will be dynamically enriched with external element transmitted using glTF. In a first step, dynamic elements will be published by external processes using glTF and added to a simple OpenUSD representation of the digital twin. In a second step the Video Content Analytics (VCA) developed at Canon CRF will generate the dynamic elements to be included in the digital twin. Finally, the possibility to enhance the visual representation of the digital twin using captured real-world data will be studied.

Skills

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AI-based annotation and querying tool of scientific publications

Duration: 4/6 months from February 2026

Internship subject

Use of Artificial Intelligence is expanding in many domains, including document processing. As the number of scientific publications grows, keeping a good understanding of a given domain becomes increasingly difficult and time-consuming for technical experts. Auto-classification of documents can help scientific teams in management and analysis of these publications by reducing the need for manual categorization. Coupled with powerful and intuitive Web interface, it can improve knowledge sharing within and between technical teams.
Advances in Artificial Intelligence (AI) and Natural Language Processing (NLP, LLMs) research have led to models that can be used as feature extractors for token-classification tasks that have been the cornerstone for Information Extraction tasks such as Named Entity Recognition and Relation Classification between entities.
The subject for this training proposal is to improve a first version of a querying and annotation tool based on popular LLMs. This tool allows keyword-based querying in a document database and AI-based summary and prompt on a selected document. The internship will consist in extending and improving this tool with more AI based features. As an example of improvement, the benefits of retrieval-augmented generation (RAG) can be studied to specialize available LLMs without re-training them. The goal is first to extract relevant and reliable information from documents using LLMs running locally, then to use this information for semantic linking of the documents to allow more advanced queries or to automate reports on a technical topic.

Mission

A first step will consist in a study of retrieval-augmented generation (RAG) to improve the information extraction from technical documents considering a technical context. An example of open questions to address could be: how to provide a technical context to different LLMs, what is the acceptable size for this context (for example as a number of documents or data size).
In a second step, the student will produce a feasibility analysis as well as a quality and performance evaluation of the RAG solution using the resources available in our premises.
Then, these results would be used for semantic linking of the documents in the database to allow more advanced queries like, for example, automated reporting on a technical topic using tools like MCP. Ideally the querying and annotation tool would be improved with the results from the above tasks.
As a result, at the end of the training period, the intern should have acquired good knowledge in the field of AI tools relevant for auto-classification of documents as well as practical usage of mainstream Python-based Web framework (Django) or APIs to programmatically interact with LLMs

[1] https://arxiv.org/abs/2406.00008 for an example of the targeted tool
[2] https://www.djangoproject.com/ the basis of the application to improve

Skills

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Simulation and Evaluation of the IEEE 802.11be/bn PHY layer

Duration: 5/6 months from February 2026

For several years, Canon has been involved in activities related to the IEEE 802.11 standard, particularly the latest WiFi-7 and WiFi-8 generations, and has a strong expertise in standardization. This internship aims to closely participate in the evolution of the Wi-Fi standard, specifically by investigating advanced technologies used in the physical layer.

Internship subject

Wi-Fi is one of the technologies that enables numerous electronic devices to exchange data or connect to the Internet wirelessly using radio waves. The main advantage of IEEE 802.11 or “Wireless LAN” devices is that they contribute for less expensive local area network (LAN) deployments. Today, millions of IEEE 802.11 devices, including those used in Canon’s devices, are utilized worldwide and operate in the same frequency bands.
The IEEE 802.11 standard is a set of specifications describing the functionalities in the MAC (Medium Access Control) layer and the physical (PHY) layer for implementing wireless local area network (WLAN) communication. To increase data rates and enhance spectral efficiency, new techniques have been introduced to boost Wi-Fi performances, reaching speeds on the order of Gbps. Advanced techniques such as OFDMA, MU-MIMO, and beamforming have significantly improved the performances in recent Wi-Fi generations, in particular IEEE 802.11ax/be.
The upcoming IEEE 802.11bn generation, also known as UHR (Ultra High Reliability), is currently under development. It aims to deliver significant improvements in throughput, latency, and packet loss, through new features such as ELR, DRU, and coordinated beamforming.
A MATLAB-based simulator used by our research center allows the evaluation of the different implemented technologies by IEEE 802.11. Indeed, this simulator enables the assessment of different techniques employed in the physical layer for various possible scenarios.
The intern will work within the Wi-Fi team and will develop a unique experience in standardization and research within a research and development center. The intern will contribute to the testing of PHY features introduced in the latest standard IEEE 802.11.

Mission

The intern will join the Wi-Fi team constituted of researchers contributing to the development of the standard IEEE 802.11. Following the work conducted on the evaluation of the IEEE 802.11ax physical layer using the WLAN Toolbox, the intern will continue the ongoing efforts within the Wi-Fi team and will be responsible for the following tasks:

Academic background

You are a Master 2 or a 5th year engineering student in telecommunications. You are curious, open-minded, passionate about new technologies and have real interpersonal skills to integrate an innovative and multicultural environment.

Specific knowledge

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