Energy
In the energy sector, AI R&D opportunities range from individual installations to energy networks.
They include, for example, forecasting production and demand, improving system efficiency or managing equipment.
Research, development and innovation
Research and development in artificial intelligence.
Would you like to explore a new use of AI for your work? Start with a concrete need, for example: monitoring an installation, working with your documents, observing crops or preparing training resources. Together, we define what is worth testing.
ResearchExperimentationInnovation
Applications
Some areas of application.
AI opens up research and development opportunities in many sectors. The areas and uses presented here provide a few examples. Other applications can be considered depending on your project.
Health
The use of medical knowledge, prevention and decision support are among the applications of AI in healthcare.
These uses can open up research and development opportunities to improve tools and services for healthcare professionals, patients and health systems.
Agriculture
The diversity of agricultural production and farming contexts can provide scope for exploring applied AI.
Crop monitoring, resource management and production planning are examples of applications for which solutions can be designed, adapted and evaluated through R&D.
Business
Using information to support decisions and organisational operations is an area of application for AI.
R&D can, for example, address knowledge management, changes in processes or the development of services suited to the needs of businesses and their customers.
Education
Supporting learners, assisting teachers and adapting learning pathways are examples of AI uses that can open up opportunities for R&D in education.
The design of educational resources and the development of learning environments illustrate these possibilities across varied educational and linguistic contexts.
Industry
Production management, quality control and maintenance illustrate some applications of AI in industry.
At the scale of a single machine or a production system, R&D can offer opportunities to design more reliable processes and equipment suited to industrial needs.
Our scientific and technical focus
4 complementary R&D areas.
Computer vision
Image and video analysis: detection, classification, segmentation and extraction of visual information. Evaluation considers data quality, model errors and the conditions in which the system will be used.
Natural language processing (NLP)
Analysing and structuring text through information extraction, classification and document search. This area focuses on understanding and using language-based content, with methods selected for the task and available data.
Generative AI and large language models (LLMs)
Content generation and task assistance using generative models, including large language models (LLMs). Topics include grounding outputs in sources, adapting to a use case and evaluating responses.
Embedded AI / Edge AI
Integrating AI into embedded systems and deploying models close to data sources. Key considerations include hardware and software integration, model efficiency and constraints relating to computing capacity, memory, energy consumption, response time and connectivity.
Propose a topic for experimentation.
Describe the question you want to investigate, the available data and your project constraints. Together, we will define the scope and terms of any potential collaboration.