Researcher Development Of Soot Models Using Machine Learning AlgorithmsAi4S - (XR36)

Job Reference: 631_24_CASE_PTG_R2

Position: Researcher Development of soot models using Machine Learning algorithms (R2) - AI4S

Closing Date: Monday, 30 September, 2024

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. 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 under one roof, and currently has over 1000 staff from 60 countries. 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.

Comprehensive research into soot formation and control remains a critical task. The significant impact of soot on human health and the environment necessitates a thorough understanding of its formation processes. Soot modeling presents a formidable challenge due to the complex nature of involved phenomena. Developing soot models capable of predicting these features without oversimplified assumptions remains a significant hurdle. 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. The applicant will join the Propulsion Technologies Group at the CASE Department of BSC with the aim of integrating AI-based ML algorithms to model soot formation and evolution.

1. Develop and implement soot modeling techniques based on ML algorithms.
2. Conduct computational studies on the interaction between turbulent combustion and soot formation.
3. Analyze and interpret simulation results, comparing them with available experimental data to assess model accuracy.
4. Collaborate with researchers at partner institutions, including data sharing and publishing results in high-impact publications.
5. Participate in the preparation of grant proposals and project reports.

Education: PhD or Master's degree in Computational Fluid Dynamics, Mechanical Engineering, Chemical Engineering, Applied Physics, or a related field.

Essential Knowledge and Professional Experience:

1. Expertise in developing and implementing soot modeling techniques using Machine Learning algorithms.
2. Deep understanding of turbulent combustion processes and their interaction with soot formation.
3. Experience with computational studies, simulation tools, and techniques for fluid mechanics and combustion modeling.
4. Proficiency in analyzing and interpreting simulation results, particularly in comparing them with experimental data to validate models.

Additional Knowledge and Professional Experience:

1. Fluency in English is essential. Proficiency in Spanish and other European languages would be advantageous.