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Job advertisement Postdocs / Research Engineer on machine learning and atmospheric composition (R2/RE2)

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European Geosciences Union

Postdocs / Research Engineer on machine learning and atmospheric composition (R2/RE2)

Postdocs / Research Engineer on machine learning and atmospheric composition (R2/RE2)


Barcelona Supercomputing Center

Barcelona, Spain




28k - 40k

Preferred education

Application deadline
31 March 2024

15 February 2024

Job description

Context And Mission

The Earth Sciences Department at the Barcelona Supercomputing Center (BSC) ( is embarking on an umbrella of large-scale activities and developments linked to the implementation of a High-Resolution Emission System for Air Quality Prediction and Greenhouse Gas Monitoring. These activities are 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.

In the frame of this ambitious project, we are offering three positions – two postdoctoral positions in machine learning and/or atmospheric modeling and one research engineer in machine learning – to work in the field of machine learning and investigate data-driven approaches to emulate and accelerate physics-driven atmospheric composition models. These developments will improve our current modeling capabilities in the field of atmospheric composition, which will open new avenues for better constraining air pollutants and greenhouse gas emissions. The successful candidates will work closely with atmospheric scientists and other research engineers from both the Atmospheric Composition (AC) and Computational Earth Science (CES) groups.

Key Duties

The main tasks of the two postdoctoral researchers include but are not limited to:

- Reviewing the existing literature on the use of machine learning for emulating and accelerating physics-driven models.

- Contributing in the preparation of large-scale atmospheric composition datasets.

- Designing, developing and evaluating data-driven emulators testing different types of machine learning models.

- Exploring avenues for improving interpretability, plausibility and physical consistency of the machine learning-based predictions.

- Publishing results in peer-reviewed journals.

- Communicating their results in workshops and conferences.

- Participating in the intellectual life of the department.

The main tasks of the research engineer include but are not limited to:

- Contributing in the preparation of large-scale machine learning datasets for atmospheric composition applications.

- Investigate, identify and implement appropriate machine learning models (including deep learning methods) to the tasks within the proposed projects.

- Provide support on their development on BSC’s High-Performance Computing (HPC) facilities.

- Learning and mastering new technologies and techniques related to machine learning.

- Contributing to scientific publications that may derive from this project.



- Postdoctoral Researchers: A PhD degree in atmospheric chemistry, physics, climate, remote sensing, environmental engineering, data science, machine learning, computer science or similar (a broad knowledge covering both machine learning and atmospheric sciences would be ideal, but candidates with expertise in only one of these two fields and interest in the other one are also welcome)

- Research Engineer: A Master degree in data science, machine learning, computer science or similar (candidates with expertise also in Earth sciences will be very valued)

Essential Knowledge and Professional Experience

Postdoctoral researchers:

- Demonstrated scientific expertise, including but not limited to a record of scholarly publications.

- Good programming skills in Python or equivalent, experience with Unix/Linux and HPC environments

Research Engineer:

- Experience in designing, developing and training machine learning models using machine learning libraries (Scikit-learn, Pytorch, Keras and/or Tensorflow)

- Proficiency in scientific programming in Python, and familiarity with working in a UNIX/Linux environment

- Knowledge on creating, manipulating and/or working with large-scale datasets

- Expertise in GPUs is not required but will be valued


- Very good interpersonal skills.

- 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.

- Fluency in English (Spanish is optional, free lessons are available at BSC)