Decadal Climate Forecaster

Job Reference845_24_ES_CVC_R2PositionDecadal Climate Forecaster (R2)Closing DateTuesday, 03 December, 2024About BSCThe 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, and is committed to facilitating scientific progress through research and development in information technologies.We are particularly interested 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.Context And MissionThis position is hosted by the Climate Variability and Change (CVC) Group within BSC's Earth Science Department. The CVC Group undertakes fundamental research to understand climate variability and change, global biogeochemical dynamics, and improving our ability to forecast climate variations and biogeochemical dynamics from one month to multiple decades into the future.The applicant will play a central role in the climate prediction team, coordinating contributions to the European project IMPETUS4CHANGE and contributing to other climate prediction activities.Key DutiesContribute to the production of initial conditions for the seasonal/decadal predictions with EC-EarthLead the production of the operational decadal predictionsContribute to the development of the next operational decadal prediction system based on EC-Earth4Develop a new blending methodology to combine decadal predictions and projectionsSupervise the work of junior members of the groupContribute to the writing of project deliverablesRequirementsEducationPhD in atmospheric science, applied mathematics, engineering, fluid dynamics or in a related disciplineEssential Knowledge and Professional ExperienceProven ability to prepare and submit manuscripts to peer-review journalsA demonstrated ability to develop experimental setups that address specific climate modeling problemsExperience in ocean/atmosphere modeling and handling climate model outputProgramming skills: scripting (e.g. bash, python), data analysis and visualization software (e.g. CDO, NCO, R, Python, NCL)Additional Knowledge and Professional ExperienceInterest and capacity in participating in the writing and leading the preparation of research and computing proposalsInterest in tutoring and/or advising master and PhD studentsCompetencesHighly collaborative spirit and ability to work as part of a large, coordinated teamConditionsThe position will be located at BSC within the Earth Sciences DepartmentWe offer a full-time contract (37.5h/week), a good working environment, flexible working hours, extensive training plan, restaurant tickets, private health insurance, and support for relocation proceduresDuration: Open-ended contract linked to the project and budget durationHolidays: 23 paid vacation days plus 24th and 31st of DecemberSalary: competitive salary commensurate with qualifications and experienceStarting date: 01/01/2025Applications procedure and processAll applications must be made through the BSC website and contain:A full CV in English including contact detailsA Cover Letter with a statement of interest in English, including two contacts for further referencesIn accordance with the OTM-R principles, a gender-balanced recruitment panel is formed for every vacancy. The panel will make a final decision and all candidates will receive feedback.DeadlineThe vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed.OTM-R principles for selection processesBSC-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).
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