Artificial intelligence (AI) and data science are profoundly transforming the way we understand, model, and interact with the world through algorithms capable of learning from data, optimizing complex decisions, and automatically detect patterns or anomalies in massive datasets. Today, they are present in fields as diverse as healthcare, the environment, physics and chemistry, the humanities and social sciences, biology, finance, industry, and digital technologies.
Artificial intelligence (AI) and data science are built on a solid interdisciplinary foundation. Mathematics andcomputer science provide the fundamental tools for modeling phenomena and analyzing algorithms.Optimization plays a key role in finding efficient solutions to complex problems, often under constraints. Probability and statistics enable us to model uncertainty, extract useful information from incomplete or noisy data, and evaluate the performance of AI methods. On the computing side, machine learning is at the heart of modern AI: these are methods capable of modeling the underlying phenomenon and automatically improving their performance based on data. Natural language processing (NLP) aims to enable machines to understand and generate human language. Databases,data mining, andknowledge extraction focus on the structuring, management, and efficient exploration of large volumes of information. Finally, signal and image processing also plays a crucial role by providing methods to automatically represent, analyze, compress, restore, or extract information from visual, audio, or temporal data—which is often noisy or incomplete.
Alongside learning-based approaches, AI has historically developed through so-calledsymbolic artificial intelligence approaches, based on explicit representations of knowledge (logical rules, ontologies, deductive reasoning). These methods allow for fine-grained interpretability and formal control over system behavior. Today, so-called hybrid approaches—which combine symbolic models and machine learning techniques—are attracting growing interest. They aim to leverage both the power of data and the richness of logical representations to build more robust, explainable, and adaptive systems.
The Faculty of Sciences draws on recognized expertise in these fields to offer high-level programs, supported by dynamic, interdisciplinary research teams. This synergy between teaching and research enables us to train students to address the scientific, technological, and ethical challenges raised by the development of AI and data science.
Read the charter on the use of artificial intelligence within the Faculty of Sciences
Programs
The Faculty of Sciences at Aix-Marseille University offers a diverse range of cutting-edge Master’s degree programs in artificial intelligence and data science, tailored to the challenges faced by businesses and the research community:
- Master’s in Computer Science – Data Science and Engineering Track (SID): Master the fundamentals and advanced technologies of processing, analyzing, and visualizing big data, while developing expertise in machine learning and data engineering. Learn more
- Master’s in Computer Science – Artificial Intelligence and Machine Learning Track (IAAA): Develop skills in deep learning, natural language processing, complex problem-solving, and knowledge modeling. Learn more
- Master’s in Signal and Image Processing (TSI): Learn innovative techniques for analyzing and modeling signals and images, serving sectors such as healthcare, aerospace, and telecommunications. Learn more
- Master’s in Applied Mathematics and Statistics (MAS) – Data Science Track: Gain expertise in statistics and machine learning to address the needs of big data and multidimensional data analysis, particularly through deep learning. Learn more
- Master’s in Mathematics and Applications (MAAP) – M2 Track in Analysis of Deterministic and Stochastic Models (Anadeal): Gain proficiency in advanced tools for mathematical analysis and modeling to solve complex problems in modeling and scientific computing, artificial intelligence, finance, risk management, and decision-making, through coursework in operational research, scientific computing, probability, and statistics. Learn more
- Master’s in Electronics, Electrical Power, and Automation (EEEA): Gain expertise in advanced techniques in automation, fault diagnosis, and energy management, incorporating artificial intelligence methods, particularly machine learning. This program prepares you for applications in process automation, systems control, operational safety of industrial facilities, and the optimization of energy production and distribution systems. Learn more
These programs share several common characteristics:
- Common objectives: They all aim to train experts capable of tackling challenges related to data analysis, machine learning, and modeling in a variety of contexts.
- Research-Based Approach: These master’s programs benefit from close ties to recognized research laboratories, offering opportunities for scientific collaboration and pursuit of a Ph.D.
- Innovative teaching methods: A project-based approach, internships, and interactions with professionals are at the heart of the curriculum.
- Target Sectors: Career opportunities include high-demand fields such as healthcare, aerospace, finance, and the digital sector.
Differences in Target Audience and Prerequisites
- Target Audience:
- Master’s in Information Science – SID and IAAA: Primarily intended for students with bachelor’s degrees in computer science or related disciplines, with a strong interest in artificial intelligence and data management.
- Master’s in Applied Sciences (MAS) – Data Science Track: Intended for students who have completed a bachelor’s degree with a focus on mathematics (B.A. in Mathematics, B.A. in Math-Info, B.A. in MIASHS, B.A. in MPCI, etc.).
- Master’s in TSI: Open to a more diverse range of backgrounds, such as students in physics, mathematics, or engineering sciences who are interested in signal and image processing.
- MAAP Master’s – Anadeal: Designed for students with a strong interest in applied mathematical analysis and probability theory.
- Master’s in EEEA: Aimed at students with a Bachelor’s in Engineering Sciences who have acquired knowledge in Automation, Electronics, Electrical Power Engineering, Mathematics, and Applied Physics.
- Admission Prerequisites:
- Master’s in Computer Science – SID and IAAA: Requires a solid grasp of the fundamentals of computer science, including programming, algorithms, and databases.
- MAS Master’s in Data Science: Requires undergraduate-level mathematics skills from a program with a strong emphasis on mathematics (e.g., Bachelor’s in Mathematics, Math-Info, MIASHS, MPCI, etc.)
- Master’s in TSI: Requires skills in mathematics and physics, as well as a foundation in programming for data processing.
- MAAP Master’s Program – Anadeal: Requires in-depth knowledge of mathematical analysis, linear algebra, and probability, along with an aptitude for modeling and theory.
These distinctions allow each master’s program to adapt to students’ specific skills and aspirations while helping them enter a variety of strategic sectors.
Research
At the Laboratory of Computer Science and Systems (LIS)
The Data Science cluster aims to bring together researchers working on data-centric issues from a computer science perspective, whether these involve data representation, manipulation, or processing. The cluster’s strength lies in its involvement of some fifty researchers covering a broad spectrum ranging from theory (machine learning, deep learning, data mining, computational linguistics) to applications (information retrieval, content recommendation, natural language processing, computer vision, bioacoustics, digital humanities, information systems, human-machine communication). The cluster is structured around four key areas:
- Artificial Intelligence and Learning
- Language and Information Retrieval
- Multimodality and Interaction
- Data Management and Mining for Knowledge Extraction
The Signal-Image Division aims to advance research in the processing, analysis, and modeling of images and signals. This research, which is both theoretical and applied, is closely linked to applications with significant societal implications. The Signal-Image Division consists of two teams, I&M and SIIM, whose research themes are both closely related and complementary. Indeed, both teams are actively engaged in research on issues related to images and modeling.
The Computation Cluster contributes to the development of theoretical and practical computer science. Its research activities cover algorithms, logic, computational models, artificial intelligence, and the geometry and topology of computation. This cluster’s research activities in the field of artificial intelligence focus on its formal and algorithmic aspects, particularly within the COALA and LIRICA teams. These activities concern, on the one hand, knowledge representation and reasoning modeling, automated proof, ranging from SAT solvers to proof systems designed for more complex logics, and issues related to constraint-based reasoning in terms of the CSP model; and, on the other hand, machine learning.
The Systems Analysis and Control division conducts research on the analysis, estimation, control, and diagnosis of systems. Activities related to AI include estimation and decision support, as well as the acquisition and representation of knowledge and reasoning for model construction,
Atthe Marseille Institute of Mathematics (I2M)
The ALEA group is divided into four interrelated teams: probability, statistics, signal and image processing, and mathematics for biology. Regarding artificial intelligence and data science, the researchers in this group work on
- statistical learning (Bayesian and variational methods, optimization)
- probabilistic and statistical modeling
- mathematical signal and image processing
- multiresolution tools (wavelets, etc.) or time-frequency analysis, data representation
- machine learning and statistics for actuarial science
- the application of statistics and machine learning in numerous fields (industrial or scientific), interdisciplinary collaborations
At theLaënnec Institute
As one of the institutes of Aix-Marseille University, the Laënnec Institute strengthens the link between research and education, promotes the commercialization of research, fosters internationalization, and encourages interdisciplinarity, all centered on artificial intelligence and digital health. The Faculty of Sciences is a key member of this institute, which aims to bring the power of artificial intelligence and digital sciences to the patient’s bedside.
In other research units
Coming soon
