Job Reference

452_26_CASE_PTG_R0

Position

Undergraduate Student –

- Computational Fluid Dynamics &
- Machine Learning (R0)

Closing Date

Wednesday, 21 October, 2026

Reference: 452_26_CASE_PTG_R0

Job title: Undergraduate Student –

- Computational Fluid Dynamics &
- Machine Learning (R0)

About BSC The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress.

BSC combines HPC service provision and R&D; into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 1000 staff from 60 countries.

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We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research. In instances of equal merit, the incorporation of the under-represented sex will be favoured.

We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences.

If you consider that you do not meet all the requirements, we encourage you to continue applying for the job offer. We value diversity of experiences and skills, and you could bring unique perspectives to our team.

Context And Mission

Over the past two decades, Computational Fluid Dynamics (CFD) has become the cornerstone of engineering design and optimization in the aerospace industry, with application in external aerodynamics, gas turbine engines and supersonic combustion, among others. Its widespread industrial adoption has been supported by Reynolds-Averaged Navier-Stokes (RANS) coupled with turbulence models, which offer a practical balance between predictive accuracy and computational cost, and by their integration with optimization algorithms for automated design. However, advances in physics-informed neural networks (PINNs), machine learning (ML) methods and high-performance computing (HPC) offer opportunities to accelerate this process.

Combining simulation data with governing equations can enable predictive surrogate models and support efficient optimization, with candidate designs verified through CFD.

This position offers the opportunity to contribute to cutting-edge developments at the intersection of aerospace engineering, computational physics, artificial intelligence, and high-performance computing, with direct relevance to future propulsion technologies.

The research team that the applicant will be involved is the Propulsion Technologies Group within the CASE Department at BSC. The team is a multidisciplinary group with researchers from all disciplines and with a strong background in Computational Fluid Dynamics (CFD). The team is involved in many EU and industrial projects related to this topic, where the successful activities and the publications in highly ranked scientific journals give the proved expertise.

Key Duties

- Development of a computational framework for scramjet intake
- Design and optimization for heterogeneous computing architectures , integrating CFD,



physics-informed machine learning, and automated geometry exploration
- The work will involve: Developing geometry parametrization and automated workflows for mesh generation and CFD simulation
- Generating and managing simulation datasets covering different intake geometries and operating conditions
- Developing and assessing PINNs (or other machine learning surrogate models) for compressible flows
- Integrating surrogate models with optimization algorithms to improve scramjet performance
- Exploiting parallel computing and GPU acceleration for machine learning workflows
- Assessing model accuracy and generalization, and validating optimized designs against independent CFD simulations and available experimental data

Requirements

- Education
- Currently enrolled in a Degree in Aerospace, Aeronautics or Mechanical Engineering with background in turbulence and combustion.

- Essential Knowledge and Professional Experience

- General knowledge of software development, numerical methods and fluid mechanics are expected.

- Additional Knowledge and Professional Experience

- Experience with machine learning and programming for HPC are not required but will be considered an asset.
- Fluency in English both written and spoken. Spanish and other European languages will be highly valuable.

- Competences

- Ability to work independently and in a team
- Proactive

Conditions

- The position will be located at BSC within the CASE Department
- We offer a part-time contract (20h/week), a good working environment, a highly stimulating environment with state-of-the-art infrastructure, adaptable working hours, extensive training plan
- Duration: linked to the educational agreement
- Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
- Starting date: 01/11/2026

Applications procedure and process All applications must be submitted via the BSC website and contain:

- A full CV in English including contact details
- A cover/motivation letter with a statement of interest in English, clearly specifying for which specific area and topics the applicant wishes to be considered. Additionally, two references for further contacts must be included. Applications without this document will not be considered.

Development of the recruitment process The selection will be carried out through a competitive examination system ("Concurso-Oposición"). The recruitment process consists of two phases:

- Curriculum Analysis: Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position.
- 40 points
- Interview phase: The highest-rated candidates at the curriculum level will be invited to the interview phase, conducted by the corresponding department and Human Resources. In this phase, technical competencies, knowledge, skills, and professional experience related to the position, as well as the required personal competencies, will be evaluated.
- 60 points. A minimum of 30 points out of 60 must be obtained to be eligible for the position.

The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women. In accordance with OTM-R principles,



a gender-balanced recruitment panel is formed for each vacancy at the beginning of the process. After reviewing the content of the applications, the panel will begin the interviews, with at least one technical and one administrative interview. At a minimum, a personality questionnaire as well as a technical exercise will be conducted during the process.

The panel will make a final decision, and all individuals who participated in the interview phase will receive feedback with details on the acceptance or rejection of their profile.

At BSC, we seek continuous improvement in our recruitment processes. For any suggestions or comments/complaints about our recruitment processes, please contact [email protected].

For more information, please follow this link.

Deadline The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will be contacted.

OTM-R principles for selection processes

BSC-CNS is committed to the principles of the Code of Conduct for the Recruitment of Researchers of the European Commission and the Open, Transparent and Merit-based Recruitment principles (OTM-R). This is applied for any potential candidate in all our processes, for example by creating gender-balanced recruitment panels and recognizing career breaks etc.

BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.

For more information follow this link

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📌 Undergraduate Student – Computational Fluid Dynamics & Machine Learning (Barcelona)
🏢 Barcelona Supercomputing Center
📍 Barcelona