Postdoctoral researcher

Postdoctoral researcher - Downscaling for Climate Services (R2) Position: Postdoctoral researcher - Downscaling for Climate Services (R2)Employer: Barcelona Supercomputing Center (BSC-CNS)Location: Les Cabanyes, SpainType: Full‑time, Experienced levelApplication deadline: 10 January 2026 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. The Climate Services (CS) Team within the Earth System Services Group (ESS) is seeking a highly motivated postdoctoral scientist to conduct cutting‑edge methodological research on statistical and machine learning downscaling methodologies for climate predictions at sub‑seasonal to decadal timescales, as well as for climate projections. The mission of the CS Team is to apply state‑of‑the‑art scientific climate knowledge for the co‑development of actionable climate information and solutions relevant for key societal sectors (e.g. agriculture, renewable energy, health) in their adaptation to climate change. The CS Team is part of the Earth System Services Group (ESS) within the Earth Sciences Department, whose mission is to research the impact of weather, atmospheric chemistry and climate upon socio‑economic sectors, including renewable energy, agriculture, water management, forest fires, urban development and health and demonstrate the ongoing value of earth system services to society and the economy. The selected candidate will identify and apply statistical and machine learning downscaling techniques to sub‑seasonal and seasonal predictions taking into account and integrating cross‑timescale differences at both regional and local scales to fulfill stakeholders needs. This includes the development of tailored tools for downscaling to contribute to ongoing projects and services. The candidate will tackle research questions related to the increase of climate predictions, spatial resolution for decision‑making and its impact in both skill and reliability of the climate information. The candidate will benefit from interdisciplinary training opportunities tailored to their experience and interests. The research will be positioned within the context of WMO’s Global Framework for Climate Services (GFCS), whose aim is to provide actionable climate information to key sectors of society. This position presents an opportunity to work alongside a wide range of leading international climate scientists delivering cutting‑edge climate services to inform policy makers across Europe and worldwide. The position holder will enjoy joining one of the leading and most dynamic European groups in the field of climate services. We are particularly interested in the strengths and lived experiences of women and under‑represented 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. Key Duties Formulate and apply statistical and machine learning algorithms to increase the spatial resolution of the sub‑seasonal and seasonal climate predictions following stakeholders requirements Interact with scientists in the group, department, center and other Disseminate research outputs in peer‑reviewed scientific papers and international conferences Apply for competitive grants and projects Engage with stakeholders and policy makers Requirements Education BSc and MSc in Physics, Environmental Sciences, Mathematics (or equivalent) Essential Knowledge and Professional Experience PhD in atmospheric sciences, applied statistics or related field Experience in data management and statistical analyses Proficiency in scientific programming in R is required and experience with Python is highly valued Excellent written and verbal communication skills in English, demonstrated in scientific publications Ability to work in a professional environment within a transdisciplinary and international team Additional Knowledge and Professional Experience Knowledge on some of the following topics: statistical downscaling, machine learning, climate data processing techniques and bias adjustments techniques Experience working with stakeholders or decision‑makers (not mandatory but desirable) Competences Problem‑solving, pro‑active, result‑oriented work attitude Excellent communication skills 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: As soon as possible #J-18808-Ljbffr