Doktorand i atmosfärsvetenskap och oceanografi, ML-metoder i klimatmodeller
Core
Developing and training data-driven machine learning models to bridge micro and global scales, improving climate model accuracy and efficiency by replacing empirical parameterizations for small-scale processes like turbulence and cloud formation.
Role type
PhD researcher in atmospheric science and oceanography (Machine Learning)
Builds
ML-enhanced climate models coupled with large-scale climate models
Domain
Climate science, atmospheric physics, oceanography, machine learning
Deliverable
production ML models
Required skills
Machine learning, climate modeling, atmospheric science, oceanography, physics, chemistry, mathematics, data analysis, numerical modeling
Preferred skills
Cross-disciplinary collaboration, communication, creativity, initiative
Technologies
Machine learning frameworks, climate models, high-performance computing
Responsibilities
Develop and train data-driven ML models, couple models to large-scale climate models, evaluate results against measurements and traditional parameterizations, estimate climate relevance of cloud effects on radiation balance.
Seniority
PhD level (Researcher)
