Research Engineer

**Job Reference**:

- 88_25_ES_CES_RE1

**Position**:

- Research engineer - Machine Learning (RE1)

**Closing Date**:

- Monday, 24 March, 2025

**Reference**: 88_25_ES_CES_RE1

**Job title**: Research engineer - Machine Learning (RE1)

**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.

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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.

**Context And Mission**
- We are seeking a highly motivated Research Engineer with expertise in machine learning, data science, or computational environmental sciences to contribute to cutting-edge research in Earth Sciences.

This position involves integrating deep learning models, refining data workflows, and contributing to predictive modeling efforts within multiple European projects, including TerraDT and CONCERTO. It builds upon methodologies established in the CERISE project and aims to generate high-resolution, temporally extended datasets to improve climate model simulations and advanced Earth system modeling capabilities.

**Key Duties**
- Implement and evaluate deep learning architectures for predictive modeling of land-use, land-cover, and climate-related variables, while developing model evaluation methodologies, including visualization and validation tools to compare deep learning models with traditional baselines.
- Refactor and modularize existing machine learning pipelines to improve scalability, efficiency, and deep learning integration.
- Work with large geospatial datasets (NetCDF, Zarr) and optimize data processing workflows for high-performance computing (HPC) environments.
- Ensure that reconstructed land-use, land-cover, and LAI datasets align with Earth system modeling requirements.
- Participate in scientific publications and technical reporting, contributing to documentation, with opportunities to be involved in academic publications and project reporting.

**Requirements**:

- Education
- Bachelor or Master’s in Computer Science, Machine Learning, Data Science, Environmental Sciences, or a related field.
- Essential Knowledge and Professional Experience
- Strong programming skills in Python, with experience in machine learning libraries such as PyTorch and Scikit-learn.
- Proven experience in developing and training machine learning models, particularly deep learning architectures.
- Strong background in handling, analyzing, and validating large-scale geospatial datasets.
- Experience working in a UNIX-based computational environment.
- Additional Knowledge and Professional Experience
- Familiarity with climate, weather, and Earth system datasets (NetCDF, Zarr).
- Experience in high-performance computing (HPC) and parallelized machine learning workflows.
- Proficiency in GPU-accelerated machine learning frameworks such as TensorFlow, RAPIDS, JAX and/ or distributed training using Dask.
- Understanding of climate and weather models.
- Competences
- Strong problem-solving and analytical skills, with the ability to optimize computational workflows.
- Ability to work independently while collaborating effectively in a research environment.
- Excellent communication skills, with a strong ability to document and present research findings.
- 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: 23 paid vacation days plus 24th and 31st of December per our collective agreement
- Starting date: 01/03/2025

**Applications procedure and process**
- 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 considere