Advanced Computing


Advanced Computing is a discipline that approaches complex computational problems from both theoretical and practical angles. It views algorithms, i.e., the human or mechanical processes of transforming data, as subjects for systematic study. The goal is to understand their limitations and capabilities and to use these insights to provide novel and more efficient algorithmic solutions. 


The specialization is addressed to students with a background in computer science, computer engineering or mathematics who can prove that they have a solid grounding in elementary algorithms, programming skills and basic knowledge of discrete mathematics. Students from other scientific and technological backgrounds are also welcome but they may be asked to take a bachelor-level course that guarantees that the requirements are covered. 

Specialization coordinator

Person Department Email
Duch Brown, Amalia CS

Specialization teachers

Person Department Email
Arias Vicente, Marta CS
Arratia Quesada, Argimiro CS
Atserias Peri, Albert CS
Balcázar Navarro, Jose Luis CS
Belanche Muñoz, Luis Antonio CS
Cortadella Fortuny, Jordi CS
Duch Brown, Amalia CS
Ferrer Cancho, Ramon CS
Lozano Boixadors, Antoni CS
Martínez Parra, Conrado CS
Messeguer Peypoch, Xavier CS
Rodriguez Carbonell, Enric CS
Roura Ferret, Salvador CS
Serna Iglesias, Maria Jose CS

Technical Competences of each Specialization


  • CEE3.1
    Capability to identify computational barriers and to analyze the complexity of computational problems in different areas of science and technology as well as to represent high complexity problems in mathematical structures which can be treated effectively with algorithmic schemes.
  • CEE3.2
    Capability to use a wide and varied spectrum of algorithmic resources to solve high difficulty algorithmic problems.
  • CEE3.3
    Capability to understand the computational requirements of problems from non-informatics disciplines and to make significant contributions in multidisciplinary teams that use computing.


Specialization compulsory subjects

Specialization complementary subjects

Master's Thesis
Mandatory Common course


Semester 1

Semester 2

Semester 3

Semester 4

Seminar activities

The activities to obtain the SIRI credits can be done in any semester of the master's degree. Consult the detail of the seminars.


Data Visualization
(VD - 6 ECTS)

Machine Learning Systems in Production
(MLOps - 6 ECTS)