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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional
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have experience with interactive visualisation of uncertainty (e.g., dashboards, uncertainty mapping). You have experience with HPC or parallel computing for computationally intensive tasks. Our offer A
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frameworks, produce pan-European maps of carbon storage potential, and contribute to interactive visualisation and dissemination of results tailored to stakeholder needs. Your key responsibilities include
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for the participation in multiple international conferences and interaction on site with project partners. Your qualities The ideal candidate: holds a PhD in a topic related to energy science, geoinformatics