Work-Study Programme in data and AI H/F

Abstract

Data is now at the heart of many digital innovations. Generated by applications, platforms, sensors and connected objects, it must be collected, prepared, analysed and leveraged in order to design smarter services and systems. This three-year apprenticeship position offers students the opportunity to progressively develop strong skills in data science, artificial intelligence and big data by contributing to concrete applied research and innovation projects. The apprentice will be involved in the different stages of Data and AI projects, from data exploration and preparation to the development and deployment of models on cloud, edge or distributed architectures. Projects may address various domains such as digital health, the Internet of Things, smart environments, industry, buildings, mobility or digital services. Prior mastery of all mentioned technologies is not required: the missions will be tailored to the apprentice’s initial level and will evolve over the three years.

Research Work

Scientific context

The widespread use of digital services, sensors and connected objects generates ever-growing volumes of data, often heterogeneous, distributed and produced continuously. Exploiting these data requires mastering the whole value chain, from collection and storage to analysis, modelling and deployment. Data science, artificial intelligence and big data therefore play a central role in designing systems capable of understanding their environment, detecting specific situations, anticipating events and supporting decision-making.

Subject

During this apprenticeship, the student will progressively contribute to the different stages of Data and AI projects. Tasks may include collecting, cleaning, structuring, exploring and visualising data from various sources, as well as designing, training and evaluating machine learning and deep learning models. Work may focus on images, time series, multimodal data or data produced by sensors, applications and connected systems.

Depending on project needs, the apprentice may also help design automated data processing pipelines, experiment with big data technologies and investigate model deployment on cloud, edge, fog or embedded architectures. The work may further extend to distributed systems, distributed AI, federated learning, data protection and the optimisation of computing resources. Application domains may include digital health, smart environments, sustainable mobility, industry, buildings and, more broadly, cyber-physical systems.

Work program

Over the three years, the apprentice will follow a progressive work program:

• Year 1 - Integration and skills development: integration into an ongoing project; data preparation, exploration and visualisation; reinforcement of python, Git and data science skills; first experiments in machine learning or deep learning; documentation and presentation of the work carried out.

• Year 2 - In-depth work and solution development: participation in more advanced projects; design of data processing pipelines; development, comparison and evaluation of AI models; exploration of big data technologies and cloud/edge/distributed architectures; production of a prototype and a literature review on the studied problem.

• Year 3 - Autonomy and valorisation : contribution, with greater autonomy, to a complete Data or AI solution ; design and optimisation of processing architectures and AI models; experiments in distributed AI, federated learning or edge computing; implementation of a demonstrator or functional prototype; valorisation of results through a report, poster, communication or scientific article.

Context

Lab presentation

CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross-cutting research; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions.

Its research is organized according to two interdisciplinary scientific teams and several application areas.

• Team 1 "Learning and Innovating" mainly concerns Cognitive Sciences, Social Sciences and Management Sciences, Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems...) on learning, creativity

  and innovation processes.

• Team 2 "Engineering and Digital Tools" mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of human-system interactions in particular through digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

• Industry 5.0,

• Construction 4.0 and Sustainable City,

• Digital Services.

Areas supported by research platforms, mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Skills

Scientific and technical skills: Basic experience in programming, strong interest in Python and software development, motivation for working with data and artificial intelligence, foundational knowledge in algorithms, databases and/or statistics, and the ability to read and understand technical resources in English. Knowledge of machine learning, deep learning, big data, IoT or distributed systems is appreciated but not mandatory.

Soft skills: Curiosity and willingness to learn, rigour, good organisational skills, ability to work as part of a team, analytical mindset, interest in experimentation and capacity to gain autonomy progressively.

Terms & Benefits

Contract: Apprenticeship, starting from October 2026

Location: CESI LILLE (Villeneuve d’Ascq)

Duration: three years, as part of an engineering degree programme or equivalent

Work-study programme only

Gross annual salary over 12 months, depending on the level of qualification being studied for

90% of health insurance costs covered;

Profit-sharing and incentive schemes;

Remote working 4 days per month (after 3 months’ service);

Meal vouchers 100% covered by CESI.