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.

The quest for carbon-neutral aviation propels us toward uncharted technological frontiers. In ERC Synergy TRANSDIFFUSE, we present an ambitious program to develop an AI-driven model that may revolutionize propulsion technologies. As a synergistic consortium, we unite the numerical modelling prowess of UPM team, the high-fidelity computational capabilities of LS/CFD group at BSC, and Purdue University experimental ingenuity.

TRANSDIFFUSE aims to develop new tools related to clean propulsion, specifically an AI-driven model called Fluid GPT. In doing so, we expect to enable major advancements to the aeronautical and power generation sectors, including, for example, hydrogen pressure gain combustion (PGC) engines, which are compact, lightweight, high-efficiency turbines. In initiating Fluid GPT, we aim to overcome challenges like those facing PGC, by exploiting the thing that has made compact turbomachinery so difficult to design: the troublesome transonic flows propelled from the combustor. With our model we aim to control and manipulate those transonic flows.

TRANSDIFFUSE will unfold in structured phases, beginning with extensive experimental and computational exploration of unsteady transonic turbine phenomena; followed by creation of Fluid GPT, informed by the physics captured in Phase I; and last, applying Fluid GPT to discover novel flow control strategies and exploit flow instabilities. Most of the tools required to achieve TRANSDIFFUSE are in the early stages of development. We intend to advance or develop theoretical modelling and computational and experimental tools as we progress through the project. Our collaborative endeavour thrives on the dynamic interplay between
computation, modelling, and experimental disciplines, ensuring that insights gleaned from each area amplify the impact and accuracy of the others.

The present Ph D proposal is contained in the Phase I researching in algorithms for detailed characterization and feature extraction including development of methods to carry out eigenmode analysis taking advantage of novel GPU platforms.