Research Engineer

**Job Reference**: - 541_25_ES_FM_RE2 **Position**: - Research Engineer - Machine Learning - Emulators (RE2) **Closing Date**: - Monday, 08 September, 2025 **Reference**: 541_25_ES_FM_RE2 **Job title**: Research Engineer - Machine Learning - Emulators (RE2) **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 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 looking for a computer scientist or a climate scientist with experience in programming to work on projects that involve Machine Learning applied to Earth Sciences. This position, therefore, presents the opportunity to work alongside a wide range of leading international machine learning experts and climate scientists delivering cutting-edge climate science across Europe. **Key Duties** - Design, develop, and evaluate data-driven emulators testing different types of machine learning models - Interact with scientists in the group and in the department and possibly conduct a broader user study in order to improve usability of the dataset created - Participate in scientific publications - Explore the use of foundation models for weather and climate prediction - Exploring avenues for improving interpretability, plausibility, and physical consistency of the machine learning-based climate emulator predictions **Requirements**: - Education - Having a degree in Telecommunication, Computer Science, Environmental Sciences or equivalent - Essential Knowledge and Professional Experience - Master's in computer science, machine learning, data science or related fields - Experience handling, analyzing and validating large datasets - Experience in developing and training Machine Learning models - Proficiency in Python, Machine Learning libraries and familiarity with working in a UNIX environment - Additional Knowledge and Professional Experience - Experience working with climate models - Experience working with climate datasets - Knowledge on some of the following topics: Seasonal climate prediction, Explainable AI, Generative Neural Networks, large-scale dataset creation - Competences - Problem-solving, pro-active, result-oriented work attitude - Capability to work in an international and fast-paced work - Good communication skills - Good written and verbal communication skills in English - Ability to work in a professional environment within a multidisciplinary and international 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 - Starting date: asap **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 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_**: The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women. 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 the