The term “digital sciences” refers to information and communication sciences relating to both hardware and software. It combines computer science, automatic control, robotics, signal processing, digital networks and communication, modelling, simulation and supercomputing.
Each scientific discipline has also developed a digital component over recent years. The digital sciences demonstrate this scientific approach based on the extensive use of computer and mathematical modelling and simulation, e.g., computational science and engineering (CSE), computational medicine, computational biology, computational archaeology and computational mechanics.
The digital revolution in motion…
In today’s world, almost everybody has heard about the digital economy. Its emergence has revolutionised access to information, transformed daily life and shaken up the ranks of several economic sectors, such as transport and tourism. Success stories have come one after another, from Le Bon Coin to BlaBlaCar and Airbnb, from taxis to banks and education.
But the digital revolution has still got a few tricks up its sleeve with substantial changes to our economic and social structures which are yet to come, for example :
• the ability to process a growing mass of information will allow for increasingly customised offers and services, even in industry
• the development of collaborative work, outside of conventional production frameworks
• digital technology will play an important role in ecological change, in particular through its development alongside the sharing economy and circular economy
The involvement of Pays de la Loire
Pays de la Loire plans to take part in this digital revolution through its assets in Research, Training and Innovation in digital sciences and through dynamic collaborative projects :
LINA and IRCCYN in Nantes, LERIA, LARIS à Angers, LIUM in Le Mans and Laval
5 areas of excellence in research
A complex system is an entity whose general behaviour cannot be explained, understood, predicted or controlled even with sophisticated knowledge of its component parts and their interactions. Typical examples are living organisms (and their ecosystems), complex industrial systems (such as a factory or aeroplane), or social systems (such as social networks or cities).
In ICT, typical examples are large software systems (complex sets of software objects), networks (such as the internet or peer-to-peer networks), and how they are used (online social networks and exchanges etc.).
In all of these contexts, the aim is to develop new methods and new tools for observing, analysing, understanding, modelling, processing, designing and controlling these systems. Research on these topics includes the robust modelling and control of uncertain systems, optimisation, artificial intelligence, and logistical problems, and they have major applications in life sciences and health.Close description
The spread of digitisation has led to substantial change in content development and handling, the enrichment of methods of interaction, and the decompartmentalization between creators and users. New practices, new models and new economic actors have modified and considerably increased the historical foundations of the content industry.
Work on these themes therefore focusses on language and speech perception, use and processing, multilingualism and multi-modality, affective computing, and virtual reality and often combines digital sciences with cognitive sciences.Close description
Software science groups together disciplines aimed at designing more powerful software, particularly with regard to response time and energy consumption. This includes, for example, software engineering for embedded systems and distributed systems, distributed algorithms, programming and modelling languages, and work on eco-design and optimized energy management in software systems.Close description
Robotics covers all techniques for designing and creating automatic machines and robots. Research focusses on the modelling, identification, control and design of the following types of robot:
Data science covers the processes involved in the utilization of data sets: acquisition, storage, query, knowledge extraction. It revolves around the following issues :
- data manipulation: collection, indexing, query, anonymization and data security
- data mining: machine learning, automation, knowledge modelling, interactions, visualisation
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