Master's degree in Computational and Data-Assisted Engineering

Barcelona School of Civil Engineering (ETSECCPB)

The master's degree in Computational and Data-Assisted Engineering provides comprehensive cross-disciplinary training in computational engineering and data science through modelling, simulation, data analysis and machine learning in industrial and scientific contexts. The programme equips students with practical skills in advanced computational tools to solve complex problems by developing new computational methods and data-driven solutions, and offers in-depth training in innovative technologies such as machine learning and big data processing.

General details

Duration and start date: 1.5 academic years, 90 ECTS credits. Starting September
Timetable and delivery: Face-to-face
Fees and grants: Approximate fees for the master’s degree, excluding other costs (does not include non-teaching academic fees and issuing of the degree certificate):
€1,743 (€4,050 for non-EU residents ).
More information about fees and payment options
More information about grants and loans
Language of instruction: English
Information on language use in the classroom and students’ language rights.
Official degree: Recorded in the Ministry of Science, Innovation and Universities

Admission

General requirements: Academic requirements for admission to master's degrees
Specific requirements: Direct admission:
This master’s degree is aimed at graduates in engineering, mathematics and physical sciences who wish to orient their career towards multidisciplinary engineering.

With bridging courses:
The academic committee of the master’s degree will assess applications from students with other degrees to determine, where needed, the specific bridging courses required (up to a maximum of 18 ECTS credits).
New intake places: 30
Pre-enrolment: Pre-enrolment period open.
Expected deadline: 01/07/2026.
How to pre-enrol
Enrolment: How to enrol
Legalisation of foreign documents: All documents issued in non-EU countries must be legalised and bear the corresponding apostille.

Curriculum

First semester

Applied Statistics and Uncertainty Quantification 5
Continuum Mechanics 5
Ethics and Communication in Science and Engineering 5
Introduction to Computational Methods 5
Partial Differential Equations and Finite Element Method 5
Scientific Programming and High Performance Computing 5

Second semester

Computational Fluid Dynamics 5
Computational Mechanics Project Laboratory 5
Computational Solid and Structural Mechanics 5
Data-Driven Project Laboratory 5
Machine Learning and Artificial Intelligence 5
Optimisation 5

Third semester

Computational Modelling of Materials 5
Coupled Problems 5
Data-Assisted Engineering and Reduced Order Modelling 5
Master's Thesis 15

CompulsoryECTS
OptionalECTS
ProjectECTS

Professional opportunities

Professional opportunities

Numerical modelling and optimisation engineer in civil engineering.
Computational simulation engineer in mechanical engineering.
Structural or fluid dynamics (CFD) analysis engineer.
Specialist in machine learning applied to engineering.
Industrial or predictive data analyst in digital technologies.
Technical innovation consultant in aerospace engineering.
Researcher or professor in academic institutions.

Competencies

Generic competencies

Generic competencies are the skills that graduates acquire regardless of the specific course or field of study. The generic competencies established by the UPC are capacity for innovation and entrepreneurship, sustainability and social commitment, teamwork, proper use of information resources, knowledge of a foreign language (preferably English) and gender perspective.

Manage complex simulations involving multiple physical phenomena accurately.
Lead cross-disciplinary teams in the development of innovative computational tools and data‑based methods to achieve a comprehensive understanding of a problem.
Implement data‑driven solutions by integrating machine learning, statistical methods and optimisation algorithms to improve the performance of engineering systems.
Incorporate principles of sustainability and ethical responsibility in engineering solutions to address the environmental and social impacts of technologies.
Critically assess environmental, social and economic impacts of products and services, promoting sustainable and socially just actions with relevant stakeholders.
Make informed and reflective decisions in complex situations, applying ethical principles in academic, professional and social contexts.
Integrate the gender perspective into discipline‑specific solutions.
Design creative solutions to social or technological problems.
Critically and responsibly analyse information sources.