Postdoc - Emulating atmospheric chemistry with artificial intelligence (R2)
Job Reference
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259\_26\_ES\_AC\_R2
Position
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Postdoc \- Emulating atmospheric chemistry with artificial intelligence (R2\)
Closing Date
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Wednesday, 03 June, 2026
Reference: 259\_26\_ES\_AC\_R2
Job title: Postdoc \- Emulating atmospheric chemistry with artificial intelligence (R2\)
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
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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.
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
In the frame of the EACH Spanish\-German project, we are looking for a postdoc to contribute advancing the development of hybrid process\-based and data\-driven air pollution models through the development of AI\-based emulators of the chemistry. Air pollution models are complex numerical tools that solve a range of chemical and physical processes to predict the evolution of the atmospheric composition (including a large number of species) in time and space. Solving numerically these different processes comes with a high computational burden, especially the chemistry components, which strongly limits some applications of interest for science or policy (e.g. doing numerous simulations to better constrain the model uncertainty, considering a more detailed representation of the chemistry). In this context, the postdoc will work on leveraging cutting\-edge deep learning models and methods for emulating different aspects of the chemistry currently solved in our in\-house MONARCH air pollution model. The training datasets will be obtained from large ensemble of simulations with our in\-house chemical box model (no spatial dimension), considering different levels of complexity and thus different number of chemical species. It will consist in a large range of simulations of the temporal evolution of the concentrations of these species. Different advanced AI architectures will be explored including for instance graph neural networks and neural operators. A specific emphasis will also be made on exploring possible approaches to enforce the physical consistency of the predictions, through soft or hard constraints. These AI\-based emulators will then be implemented in the MONARCH air pollution to assess their performance (skills and speed\-up) in real conditions. This research will contribute advancing toward a more accurate and computational cheaper representation of the chemical processes in modern air pollution models, with possible uptake into other models like chemistry\-climate models where the chemistry is typically even more simplified due to the needs for multi\-decadal simulations.
See https://www.youtube.com/watch?v\=EE8PijwMFXM for a general presentation of our Atmospheric Composition group. In the AC group and BSC\-ES department, the successful candidate will have the chance to work in a diverse, international and highly collaborative environment. He/she will have access to the cutting\-edge computational resources of Marenostrum 5, one of the largest supercomputer in Europe.
Key Duties
- Advance our understanding and modeling capabilities in atmospheric sciences leveraging AI technologies.
- Collaborate with interdisciplinary teams (e.g., atmospheric scientists, data scientists, HPC engineers).
- Publish and present research findings in high\-impact scientific journals and conferences, contributing to the advancement of AI applications in atmospheric sciences.
- Utilize BSC’s high\-performance computing resources (e.g., MareNostrum 5\) for large\-scale AI model training and optimization.
- Document code, workflows, and results following best practices in software development, version control (e.g., Git), and reproducible research.
- Engage in outreach and dissemination activities to promote the project’s objectives and foster collaboration with partners.
- Education
- Essential Knowledge and Professional Experience
+ Excellent knowledge in deep learning
+ Experience with Pytorch (or similar) deep learning framework
+ Fluency in English is essential, Spanish is optional (free lessons available after joining)
- Additional Knowledge and Professional Experience
+ Experience with Git or similar software version control will be valued
+ Experience with coding and documentation best practices and standards
+ Experience with parallel programming and/or distributed training across multiple GPUs will be valued
+ Experience in atmospheric sciences will be valued
- Competences
+ Excellent written and verbal communication skills
+ Ability to organize the work, document the advancement and present results
+ Ability to take initiative, prioritize and work under set deadlines
+ Ability to work both independently and within a team
Conditions
- The position will be located at BSC within the Earth Sciences Department
- We offer a full\-time contract (35h/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, support to the relocation procedures
- Duration: Open\-ended contract due to technical and scientific activities linked to the project and budget duration
- Holidays: 22 days of holidays \+ 6 personal days \+ 24th and 31st of December per our collective agreement
- Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
- Starting date: July 2026
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.
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.*
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 Commiss
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