Postdoc to develop physically consistent super-resolution downscaling deep learning models for [...]

Overview Postdoc to develop physically consistent super-resolution downscaling deep learning models for atmospheric composition (R2) at Barcelona Supercomputing Center (BSC-CNS).

Responsibilities 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 in the intellectual life of the group

Present model developments and research findings, contribute to scientific publications, and other duties as assigned

Qualifications / Requirements Education PhD (or MSc with strong experience) in computer science, data sciences, Earth sciences, applied mathematics, physics, or related discipline

Essential Knowledge and Professional Experience Strong 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 Experience Experience 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)

Competences Very 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

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. 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. The group develops and applies numerical models over multiple scales, from weather to climate and from global to urban scales. MONARCH, a cutting-edge atmospheric composition model, is part of the Copernicus CAMS system and is used in both research and operational activities. This activity is part of a large EU initiative on modernization and digitalization of observation networks.

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

Conditions The position will be located at BSC within the Earth Sciences Department

Full-time contract (37.5h / week) with a stimulating environment, state-of-the-art infrastructure, flexible hours, training plan, and benefits

Duration : Open-ended contract linked to project and budget duration

Holidays : 23 paid vacation days plus 24th and 31st December per collective agreement

Salary : competitive and commensurate with qualifications and Barcelona cost of living

Starting date : Pending

Applications procedure 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 the areas / topics of interest. Additionally, two references for further contacts must be included.

Equity, Diversity and Inclusion : We promote equity, diversity and inclusion and encourage applications from underrepresented groups. We are an equal opportunity employer.

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 are followed, and a gender-balanced recruitment panel is formed for each vacancy.

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