Master student

hace 2 semanas


Barcelona, España Barcelona Supercomputing Center A tiempo completo

Context And Mission The Department of Earth Sciences of the Barcelona Supercomputing Centre-Centro Nacional de Supercomputación (BSC-CNS), BSC-ES henceforth is one of the most active groups in air quality and atmospheric composition modeling, climate prediction, and climate services in Europe. The Department comprises about 200 people,including scientists and technical staff. It is structured into five distinct but interacting research groups: Atmospheric Composition, Climate Prediction, Earth System Services (ESS), Global Health Resilience, and Computational Earth Sciences.Successful candidates will benefit from the training program and the BSC-CNS staff benefits: an international multidisciplinary scientific environment, advanced research training, and advanced computational facilities.We are seeking a highly motivated student in computer science, environmental sciences, or a related field to contribute to cutting-edge research in machine learning applications for Earth Sciences.This position offers the opportunity to contribute to the advancement of machine-learning–driven reconstructions of land surface boundary conditions for Earth system modeling. The selected candidate will support the analysis and optimization of scalable workflows within AI4Land, a project focused on improving representations of the carbon and water cycles by generating high-resolution reconstructions of land use, land cover, and leaf area index. By evaluating performance bottlenecks, improving data throughput, and optimizing training and inference pipelines, the selected candidate will help enable faster experimentation cycles and more reliable production of long-term, high-resolution land surface datasets.This master’s thesis project will provide hands-on experience in performance profiling, workflow optimization, and the integration of machine learning methods into large-scale geoscientific applications. The role offers a dynamic, interdisciplinary environment with close collaboration between climate scientists, machine learning researchers, and computational engineers, and will contribute to open, reproducible research that supports next-generation Earth system modeling. Key Duties Launch and monitor machine learning training and inference pipelines used to generate high-resolution land surface reconstructions within the AI4Land project. Analyze the computational performance of the ML workflows, including profiling resource usage, evaluating data throughput, and assessing scalability across computing environments. Identify performance bottlenecks and collaborate with project scientists to propose and implement workflow optimizations. Participate in regular project meetings and collaborative activities to present findings, gather feedback, and support the alignment of workflow improvements with scientific objectives. Contribute to documentation, technical reports, and workflow descriptions to ensure transparency, reproducibility, and effective knowledge


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    hace 1 semana


    Barcelona, Barcelona, España Barcelona Supercomputing Center A tiempo completo

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    hace 2 días


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    hace 1 semana


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    hace 1 semana


    Barcelona, Barcelona, España Barcelona Supercomputing Center (BSC) A tiempo completo

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    hace 1 semana


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    hace 3 días


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    hace 4 días


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