Graduate Schools

Graduate School in Computing Science (GRASCOMP)

The scientific objectives of GRASCOMP are :

  • To provide computer science PhD students with high level scientific support, in the Belgium's French Community ;
  • To organize a large range of advanced training courses in various domains of computing science including emergent domains ;
  • To organize a transversal training to scientific communication: technical writing and oral presentation ;
  • To facilitate PhD student's mobility and their integration in the international scientific community, through participation ot seminars, summer schools, workshops and international meetings ;
  • To foster exchange between PhD students in the Belgium's French Community and between research team working on related topics.

Graduate School in Systems, Optimization, Control and Networks (SOCN)

This graduate school provides fundamental and advanced courses in systems and control theory in optimization and in networks, or related areas. 

The aim is to give an overview of recent research developments and applications in the field and yet to address a reasonably wide audience. Background in systems and control can be assumed, but the lecturers do mention in their course description what background will be required.

Graduate School on MUltimedia, SIlicon, Communications, Security : Electrical and Electronics Engineering (MUSICS)

The scientific objectives of the Graduate School MUSICS relate to the fields of electricity, electronics and information technology. The scientific objectives can be summarized as the necessary «tools, methods, algorithms, circuits, devices and technologies that are used to acquire, treat, analyze, protect, transmit or store signals and information, as well as the production, transport and transformation of electric energy».

Computational Intelligence and Learning (CIL)

Today, based on the advances in IT and digital data storage, in many industrial, economic, medical or other application areas, increasing amounts of signals, measurements, images and other types of data become available, implicitly describing underlying processes or structures. With this availability the potential - and need - arises for advanced intelligent tools to extract the underlying information, predict, diagnose, estimate or make use of it in some other way, in order to optimize or improve services. Since structure in this data is mostly hidden under noise, due to the stochastic nature of the processes and their measurement, robust and adaptive tools are needed that can cope with this nature.

"Computational intelligence and learning" intends answering to this need; it gathers research work carried out in various disciplines, with the objective of adding some form of intelligence or automatic learning of situations and properties in algorithms, data processing tasks, data mining, information extraction, etc. Computational intelligence and learning concerns disciplines and concepts such as machine learning, artificial neural networks, deep learning, artificial intelligence, data mining, fuzzy logic, evolutionary computation, probabilistic techniques.