Designing and training surrogate models (emulators) for biogeochemical modules within ESMs; Quantifying the response of the biological carbon pump to changing environmental drivers; Integrating observational datasets (e.g., satellite data, in situ measurements) with model output; Collaborating with climate scientists, oceanographers, and data scientists within national and international consortia; Publishing research in peer-reviewed journals and presenting at international conferences
Requirements:
A PhD in oceanography, climate science, applied mathematics, computational science, or a related field; Experience with Earth System or biogeochemical modelling; Strong programming skills (e.g., Python, R, MATLAB) and experience with advanced machine learning modelling; A strong interest in interdisciplinary collaboration and addressing complex Earth system questions
Text:
Postdoc in Earth System Models emulation with machine learning Designing and training surrogate models (emulators) for biogeochemical modules within ESMs; Quantifying the response of the biological carbon pump to changing environmental drivers; Integrating observational datasets (e.g., satellite data, in situ measurements) with model output; Collaborating with climate scientists, oceanographers, and data scientists within national and international consortia; Publishing research in peer-reviewed journals and presenting at international conferences A PhD in oceanography, climate science, applied mathematics, computational science, or a related field; Experience with Earth System or biogeochemical modelling; Strong programming skills (e.g., Python, R, MATLAB) and experience with advanced machine learning modelling; A strong interest in interdisciplinary collaboration and addressing complex Earth system questions
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