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
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. 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.
Context And Mission
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.
Key Duties Develop and implement soot modeling techniques based on ML algorithms. Conduct computational studies on the interaction between turbulent combustion and soot formation. Analyze and interpret simulation results, comparing them with available experimental data to assess model accuracy. Collaborate with researchers at partner institutions, including data sharing and publishing results in high-impact publications. Participate in the preparation of grant proposals and project reports. Requirements 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:
Expertise in developing and implementing soot modeling techniques using Machine Learning algorithms. Deep understanding of turbulent combustion processes and their interaction with soot formation. Experience with computational studies, simulation tools, and techniques for fluid mechanics and combustion modeling. Proficiency in analyzing and interpreting simulation results, particularly in comparing them with experimental data to validate models. Additional Knowledge and Professional Experience:
Fluency in English is essential. Proficiency in Spanish and other European languages would be advantageous. Knowledge of computational tools and languages, such as Fortran, Python, or similar. Familiarity with high-performance computing environments for running large-scale simulations. Understanding of combustion chemistry and reaction kinetics relevant to soot formation. Competences:
Ability to work in a team and in a multi-cultural environment. Capability to address challenges in modeling and simulations related to turbulent combustion and soot formation. Conditions The position will be located at BSC within the CASE Department. We offer a full-time contract (37.5h/week), a good working environment, flexible working hours, extensive training plan, restaurant tickets, private health insurance. Duration: 4 years. Holidays: 23 paid vacation days plus 24th and 31st of December per our collective agreement. Salary: 45.000,00€. Additional Expenses Grant: Each fellowship will be associated with a grant for additional expenses. Starting date: asap - the incorporation for this vacancy must be before the 16th of December 2024. 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. The selection will be carried out through a competitive examination system. The recruitment process consists of two phases:
Curriculum Analysis: Evaluation of previous experience and/or scientific history. Interview phase: The highest-rated candidates will be invited to the interview phase. Deadline
The vacancy will remain open until a suitable candidate has been hired.
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.
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