Postdoctoral Researcher In Artificial Intelligence Applied To Climate Services (R2) - Ai4S

.Job Reference 632_24_ES_CS_R2Position Postdoctoral Researcher in Artificial Intelligence Applied to Climate Services (R2) - AI4SClosing Date Monday, 30 September, 2024About 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, and is a founding member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe). The mission of BSC is to research, develop and manage information technologies 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, currently employing over 1000 staff from 60 countries.Context And Mission The role of the postdoc in Artificial Intelligence applied to Climate Services takes place in a rapidly evolving field where the integration of AI and machine learning is transforming how climate data is processed, analyzed, and applied. This position is situated within a research group focused on developing cutting-edge methods for climate model calibration, validation, and predictive skill enhancement. The postdoc will contribute to the ongoing efforts to improve climate services by utilizing explainable AI techniques and integrating them into the BSC ecosystem, which supports environmental and climate research.Key DutiesApply explainable Machine Learning to local and non-local calibration methods for climate models.Identify windows of opportunity for increased predictive skill using Machine Learning.Support the development, testing, streamlining, and documentation of new code within the BSC software ecosystem for model validation, calibration, and forecast verification.RequirementsEducation PhD in Climate Sciences, Environmental Sciences, Physics, Mathematics, or related fields.Essential Knowledge and Professional Experience Proficiency in object-oriented programming, preferably in R or Python.Understanding of Explainable Machine Learning and its applications in climate models.Familiarity with calibration methods (local and non-local) and forecast verification.Additional Knowledge and Professional Experience Fluency in English is essential. Proficiency in Spanish and other European languages would be advantageous.Experience in High-Performance Computing (HPC) environments (preferred, but not required).Knowledge of predictive skill evaluation techniques in climate models.Competences Ability to work in a team and in a multi-cultural environment.Ability to explain and interpret Machine Learning models in the context of climate services.Skills in developing, testing, and documenting code within a collaborative software ecosystem.Strong communication skills for collaboration with multidisciplinary teams.ConditionsThe position will be located at BSC within the Earth Sciences Department.We offer a full-time contract (37