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.

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Nowadays considerable effort is put on making renewable energies a practical technology in the transportation sector, despite most propulsion technologies still rely on fossil fuel combustion. Among the different solutions proposed to alleviate the impact of fossil fuel combustion and move towards the decarbonization of transport and energy sectors, common fuel mixtures are generally blended with oxygenate additives in order to improve properties of conventional hydrocarbon fuels and contribute to a more sustainable transportation market. For instance, gasoline oxygenated blends generally show improved performance with respect to common gasoline mixtures in terms of combustion and emissions characteristics, by burning leaner and reducing the emissions. The additives however impact fuel physicochemical properties like distillation curve, vapor pressure, freezing point and octane numbers. For instance, ethanol is known as an octane number enhancer and has proven to reduce combustion emissions. Concurrently, the increased use of Artificial Intelligence (AI) and Machine Learn-ing (ML) methods observed in recent years in the combustion community, can be exploited to minimize produc-tion of costly simulations and allow exploration of large data. ML models have already found application in the prediction of injection spray angles of jets in aviation engines and of physico-chemical properties of fuel and blends of practical interest in the aviation industry.

Within this context, the applicant will lead the development and implementation of a ML algorithm for the pre-diction of fuel mixture properties. The developed tool aims at efficiently predicting fuel properties for common gasoline-oxygenated fuels and kerosene mixtures, combining experimental databases with thermodynamic laws. Effect of blends and blending ratios on physico-chemical properties like cetane number, flash boiling or freezing point will be examined by parametrically varying the values of the mentioned quantities in a range of interest.

The activity seeks at boosting use of AI and ML models in combustion problems. The applicant will join the Pro-pulsion Technologies Group (PTG), a research group from the Computer Applications in Science and Engineer-ing (CASE) Department at the Barcelona Supercomputing Center. As part of the PTG, the applicant will form part of a multidisciplinary team of researchers with a strong background on Computational Fluid Dynamics (CFD) applied to high-fidelity simulations for the development of clean propulsion and power generation sys-tems. The PTG is actively involved in several European research-oriented and industrial projects for which results are disseminated in highly ranked scientific journals and conferences.