Master student

718_25_ES_CES_R0

Master student - Performance of Machine Learning applied to Earth Sciences (R0) - AI4Land

Sunday, 21 December, 2025

Reference: 718_25_ES_CES_R0

Job title: Master student - Performance of Machine Learning applied to Earth Sciences (R0) - AI4Land

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

The Department of Earth Sciences of the Barcelona Supercomputing Centre-Centro Nacional de Supercomputación (BSC-CNS), BSC-ES henceforth ) is one of the most active groups in air quality and atmospheric composition modeling, climate prediction, and climate services in Europe. The Department comprises about 200 people,

including scientists and technical staff. It is structured into five distinct but interacting research groups: Atmospheric Composition, Climate Prediction, Earth System Services (ESS), Global Health Resilience, and Computational Earth Sciences.

Successful candidates will benefit from the training program and the BSC-CNS staff benefits: an international multidisciplinary scientific environment, advanced research training, and advanced computational facilities.

We are seeking a highly motivated student in computer science, environmental sciences, or a related field to contribute to cutting-edge research in machine learning applications for Earth Sciences.

This position offers the opportunity to contribute to the advancement of machine-learning–driven reconstructions of land surface boundary conditions for Earth system modeling. The selected candidate will support the analysis and optimization of scalable workflows within AI4Land, a project focused on improving representations of the carbon and water cycles by generating high-resolution reconstructions of land use, land cover, and leaf area index.

By evaluating performance bottlenecks, improving data throughput, and optimizing training and inference pipelines, the selected candidate will help enable faster experimentation cycles and more reliable production of long-term, high-resolution land surface datasets.

This master's thesis project will provide hands-on experience in performance profiling, workflow optimization, and the integration of machine learning methods into large-scale geoscientific applications. The role offers a dynamic, interdisciplinary environment with close collaboration between climate scientists, machine learning researchers, and computational engineers, and will contribute to open, reproducible research that supports next-generation Earth system modeling.

Key Duties

• Launch and monitor machine learning training and inference pipelines used to generate high-resolution land surface reconstructions within the AI4Land project.
• Analyze the computational performance of the ML workflows, including profiling resource usage, evaluating data throughput, and assessing scalability across computing environments.
• Identify performance bottlenecks and collaborate with project scientists to propose and implement workflow optimizations.
• Participate in regular project meetings and collaborative activities to present findings, gather feedback, and support the alignment of workflow improvements with scientific objectives.
• Contribute to documentation, technical reports, and workflow descriptions to ensure transparency, reproducibility, and effective knowledge transfer across project teams and partners.

Requirements

• Education
• Enrolled in a Master's program in Computer Science, Data Science, Computational Engineering, Environmental Sciences, or a related field.
• Essential Knowledge and Professional Experience
• Strong programming skills in Python.
• Experience with UNIX-based environments and command-line workflows.
• Good written and verbal communication skills in English
• Ability to work in a professional environment within a multidisciplinary and international team
• Additional Knowledge and Professional Experience
• Experience developing modular and reproducible data workflows.
• Familiarity with container technologies (e.g., Docker, Singularity, Podman).
• Working knowledge of High-Performance Computing (HPC) environments and batch schedulers (e.g., SLURM).
• Experience with workflow management tools.
• Familiarity with data formats commonly used in climate and Earth system science (e.g., NetCDF, Zarr).
• Basic understanding of AI/ML model execution and integration.
• Competences
• Strong problem-solving and analytical skills.
• Ability to work independently and proactively in a research environment.
• Effective communication and teamwork skills.
• Proficiency in written and spoken English.

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: 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: asap

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