Researcher Development of Soot Models Using Machine

**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**Reference**: 631_24_CASE_PTG_R2**Job title**: Researcher Development of soot models using Machine Learning algorithms (R2) - AI4S**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, 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 EuroHPC 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.

Look at the BSC experience:
BSC-CNS YouTube Channel
Let's stay connected with BSC Folks

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**

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 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.**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, for implementing and testing modeling techniques.
- 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, a highly stimulating environment with state-of-the-art infrastructure, 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: 45.00,00€
- Additional Expenses Grant: Each fellowship will be associated with a grant for additional expenses, such as IT equipment, travel, training, stays, etc.
- Starting date: asap - the incorporation for this vacan