The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses Mare Nostrum, 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 Euro HPC 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.

Comprehensive research into soot formation and control remains a critical task. The significant impact of soot on human health and the environment, coupled on the other hand to the interest on its synthesis for industrial applications, necessitates a thorough understanding of its formation processes. Soot modeling presents a formidable challenge due to the complex and multi-scale nature of involved phenomena. Accurate prediction requires capturing a wide range of processes, from fuel decomposition and particle inception to surface growth, aggregation, and oxidation. These processes are highly sensitive to variables like temperature, pressure, and fuel composition, making accurate prediction across diverse conditions arduous. Furthermore, the dynamic evolution of soot particle surface properties affects growth and oxidation rates. The fractal-like structures of soot particles, challenging to simulate, require advanced computational methods to track particle size distribution, morphology, and internal structure. Developing soot models capable of predicting these features without oversimplified assumptions remains a significant hurdle.

Traditional soot modeling approaches often rely on computationally expensive numerical methods to solve complex chemical kinetics and fluid dynamics associated with soot production, particularly in turbulent flows. Machine learning (ML) is emerging as a promising alternative. By leveraging datasets from simulations and/or experiments, ML techniques identify patterns to predict soot behavior with significantly reduced computational overhead. Trained on key parameters like temperature, gas composition, and pressure histories, ML models can accelerate accurate soot modeling in both laminar and turbulent flames, enabling the optimization of combustion processes while minimizing emissions.

The applicant will join the Propulsion Technologies Group at CASE Department of BSC with the aim of integrating AI-based ML algorithms to model soot formation and evolution. The resulting modeling framework will be implemented in the in-house solver Alya and will be validated in the context of high-fidelity Large Eddy Simulations (LES) in turbulent combustion applications.

The funding for these actions/fellowships and contracts comes from the European Union Recovery and Resilience Facility - Next Generation, within the framework of the General Invitation by the public business entity Red.es to participate in the talent attraction and retention programs within Investment 4 of Component 19 of the Recovery, Transformation, and Resilience Plan.
For more information, please check: https://www.bsc.es/join-us/excellence-career-opportunities/ai4s