Research Engineer/Postdoc for Atmospheric Composition team (RE2/R2)

Reference: 415_25_ES_AC_RE2
Job title: Research Engineer/Postdoc for Atmospheric Composition team (RE2/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
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

We are looking for software atmospheric modeler to join the Atmospheric Composition group within the Earth Sciences department at the BSC-CNS. Composed of about 50 members (research engineers, predocs, postdocs, senior scientists), the AC group aims at better understanding and predicting the spatiotemporal variations of atmospheric pollutants along with their effects upon air quality, weather and climate (see a video presentation of the group in). This is addressed through the continuous development and application of numerical models over multiple scales, from weather to climate and from global to urban scales. The AC group is the research backbone of the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH), a cutting-edge atmospheric composition model used for both research and operational activities, that contains advanced chemistry and aerosol packages coupled online with a meteorological driver. MONARCH is part of the ensemble Copernicus Atmospheric Monitoring Service (CAMS) regional air quality forecasting system that provides operational forecast and analysis over Europe. CAMS is a key component of the European Union’s Earth observation system. MONARCH also runs operationally at the first World Meteorological Organization (WMO) Barcelona Dust Regional Center (BDRC) for Northern Africa, the Middle East and Europe, and the International Cooperative for Aerosol Prediction (ICAP) ensemble of global aerosol forecasts.
Exploitation of novel high-resolution emission inventories requires going toward higher spatial resolution to prepare the next generation of air quality predictions in Europe. We are seeking a highly motivated Research Engineer or Postdoctoral Researcher to join our team in the development of a physically consistent super-resolution downscaling deep learning models for chemical transport models (CTMs), able to predict high-resolution atmospheric composition fields (1-3 km) from coarse regional simulations (10-20 km). He/she will explore, adapt and improve cutting-edge deep learning architectures proposed in the field of computer vision and weather/climate sciences, including image- and video-based convolutional neural networks. Different strategies will be investigated to improve the physical consistency of the predicted high-resolution concentration fields, using soft and/or hard constraints. More sophisticated types of machine learning including for instance graph neural networks will also be explored. This research will start focusing on the Iberian Peninsula but should then scale to European scale (and in the future, to global scale).

The successful applicant will be part of an active group of researchers focusing on leveraging deep learning technologies to address key scientific and policy-oriented challenges (currently about 7 people in the AC group, about 15-20 in the BSC Earth Sciences department). To conduct this research, he/she will have access to the groundbreaking High-Performance Computing infrastructure of BSC, notably MareNostrum 5, one of the most powerful supercomputers in Europe, with a peak performance of 314 Pflops, 200 PB of storage and 400 PB of active archive, and an accelerate partition including 1120 nodes composed of 4 NVIDIA GPUs nodes. The candidate will also benefit from the collaboration with the Computational Earth Sciences group of the department, composed of experts in HPC, software development, and AI.

This activity is part of a large initiative on the “Modernization of observation networks and digitalization of production processes for the development of intelligent meteorological services in the context of climate change” in the framework of the European Recovery, Transformation, and Resilience Plan funded by the European Union-Next Generation EU.

Key Duties

Develop and implement deep learning architectures (e.g., CNNs, GANs, diffusion models) for spatial super-resolution of atmospheric composition fields generated by atmospheric chemistry models
Train and validate models using historical high-resolution observational datasets and CTM outputs
Integrate physical constraints and uncertainty estimation within the machine learning workflow
Collaborate closely with atmospheric scientists, and participate to the intellectual life of the group
Present model developments and research findings, contribute to scientific publications, and other duties as assigned

Requirements

EducationPhD (or MSc with strong experience) in computer science, data sciences, Earth sciences, applied mathematics, physics, or related discipline
Essential Knowledge and Professional ExperienceStrong background in deep learning, especially in image super-resolution or geospatial applications
Experience with ML frameworks (e.g., PyTorch, TensorFlow) and high-performance computing environments
Demonstrated expertise in designing and implementing machine learning models from scratch
Excellent programming skills in Python
Additional Knowledge and Professional ExperienceExperience working in HPC environment (including bash)
Experience in Earth sciences will be valued
Experience with graph neural networks will also be valued
Experience with revision control systems (e.g., SVN or Git)
CompetencesVery good interpersonal skills
Fluency in English
Excellent written and verbal communication skills
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 (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, support to the relocation procedures
Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration
Holidays: 23 paid vacation days plus 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: Pending

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@bsc.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