Job Reference
629_24_CASE_PTG_R3
Position
Researcher on Development of advanced methods for hydrogen combustion using ML-based algorithms (R3) - AI4S
Data de tancament
Dilluns, 30 Setembre, 2024
Reference: 629_24_CASE_PTG_R3
Job title: Researcher on Development of advanced methods for hydrogen combustion using ML-based algorithms (R3) - 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 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.
Look at the BSC experience:
BSC-CNS YouTube Channel
Let's stay connected with BSC Folks!
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.
Context And Mission
The importance of hydrogen combustion in the decarbonization of the power and transport sectors has impelled its research in order to achieve ultra-low emission burners and avoid instabilities that may compromise the combustion chamber. However, the peculiarities of hydrogen, particularly its high diffusivity, may lead to preferential diffusion effects consisting of higher flame speeds, promotion of flame instabilities, achievement of superadiabatic temperatures with the subsequent effect on emissions, flame stability and thermos-acoustics.
On the one hand, the exponential increase in computational power experienced in the last years has opened the door to accurately simulate industrial devices in feasible amounts of time through Large Eddy Simulations (LES), which arise as a cost-effective option for design and analysis. On the other hand, such increase in computational resources has in turn allowed to gather large amounts of data that can be post-processed to reveal hidden patterns through machine learning or, alternatively, the generation of digital twins that accurately reproduce the response of the real system.
This postdoctoral position is intended to apply numerical simulations in the frame of Large Eddy Simulations (LES) to the aforementioned problems extending the capabilities of conventional physics-based approaches with Artificial Intelligence. For the simulations the Flamelet Generated Manifold (FGM) combustion model will be used due to its accurate description of the flame in the typical operating conditions of thermal engines and ML-based subgrid models will be developed from existing numerical databases. Insights in the flame behaviour will be achieved through the application of data-driven methods, comprising Modal Decomposition techniques along with Machine-Learning based algorithms to generate digital twins.
The research team in which the applicant will be involved is the Propulsion Technologies Group at CASE Department of BSC. The team is a multidisciplinary group with researchers from all disciplines and with strong background in Computational Fluid Dynamics (CFD). The team is involved in several EU and industrial projects related to this topic, where the successful activities and the publications on highly ranked scientific journals give the proved expertise.
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.
For more information, please check: https://www.bsc.es/join-us/excellence-career-opportunities/ai4s
On the one hand, the exponential increase in computational power experienced in the last years has opened the door to accurately simulate industrial devices in feasible amounts of time through Large Eddy Simulations (LES), which arise as a cost-effective option for design and analysis. On the other hand, such increase in computational resources has in turn allowed to gather large amounts of data that can be post-processed to reveal hidden patterns through machine learning or, alternatively, the generation of digital twins that accurately reproduce the response of the real system.
This postdoctoral position is intended to apply numerical simulations in the frame of Large Eddy Simulations (LES) to the aforementioned problems extending the capabilities of conventional physics-based approaches with Artificial Intelligence. For the simulations the Flamelet Generated Manifold (FGM) combustion model will be used due to its accurate description of the flame in the typical operating conditions of thermal engines and ML-based subgrid models will be developed from existing numerical databases. Insights in the flame behaviour will be achieved through the application of data-driven methods, comprising Modal Decomposition techniques along with Machine-Learning based algorithms to generate digital twins.
The research team in which the applicant will be involved is the Propulsion Technologies Group at CASE Department of BSC. The team is a multidisciplinary group with researchers from all disciplines and with strong background in Computational Fluid Dynamics (CFD). The team is involved in several EU and industrial projects related to this topic, where the successful activities and the publications on highly ranked scientific journals give the proved expertise.
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.
For more information, please check: https://www.bsc.es/join-us/excellence-career-opportunities/ai4s
Key Duties
- Develop tools for data-driven methods to improve the modeling of turbulent combustion in hydrogen flames.
- Implement machine learning techniques to contribute to the accurate modeling of hydrogen combustion in engine-relevant conditions.
- Collaborate with interdisciplinary teams to integrate computational models with experimental data for enhanced combustion simulations.
- Optimize combustion models for high-performance computing environments to ensure efficiency and scalability in simulations.
Requirements
-
Education
- The candidate should hold a PhD Degree in Aerospace, Aeronautics or Mechanical Engineering with background in turbulence and combustion.
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Essential Knowledge and Professional Experience
- Strong foundational knowledge in fluid mechanics.
- Expertise in Large Eddy Simulation (LES) techniques.
- Solid understanding of numerical methods for scientific computing.
- Knowledge of combustion chemistry and its application in modeling.
- Familiarity with machine learning techniques applied to scientific research.
-
Additional Knowledge and Professional Experience
- Fluency in English is essential. Proficiency in Spanish and other European languages would be advantageous.
- Computational skills and parallel programming for HPC
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Competences
- Ability to work in a team and in a multi-cultural environment.
- Ability to work independently and make decisions
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: 55.00,00€
- 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 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. Additionally, two references for further contacts must be included. Applications without this document will not be considered.
Development of the recruitment process
The selection will be carried out through a competitive examination system ("Concurso-Oposición"). The recruitment process consists of two phases:
- Curriculum Analysis: Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position. - 40 points
- Interview phase: The highest-rated candidates at the curriculum level will be invited to the interview phase, conducted by the corresponding department and Human Resources. In this phase, technical competencies, knowledge, skills, and professional experience related to the position, as well as the required personal competencies, will be evaluated. - 60 points. A minimum of 30 points out of 60 must be obtained to be eligible for the position.
The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women.
In accordance with OTM-R principles, a gender-balanced recruitment panel is formed for each vacancy at the beginning of the process. After reviewing the content of the applications, the panel will begin the interviews, with at least one technical and one administrative interview. At a minimum, a personality questionnaire as well as a technical exercise will be conducted during the process.
The panel will make a final decision, and all individuals who participated in the interview phase will receive feedback with details on the acceptance or rejection of their profile.
At BSC, we seek continuous improvement in our recruitment processes. For any suggestions or comments/complaints about our recruitment processes, please contact recruitment [at] bsc [dot] es.
For more information, please follow this link.
Deadline
The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will be contacted.
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 and the Open, Transparent and Merit-based Recruitment principles (OTM-R). This is applied for any potential candidate in all our processes, for example by creating gender-balanced recruitment panels and recognizing career breaks etc.
BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.
For more information follow this link
BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.
For more information follow this link