2 PhD positions (m/f/d) In Model-Driven Machine Learning for Climate and Earth Science
The Model-Driven Machine Learning group aims to understand and predict the complex dynamics of earth’s atmosphere, ocean, land and ice. Physics-based simulators handle this complexity well, but struggle with data assimilation, parameter tuning and uncertainty quantification. Machine learning thrives on large datasets, but ignores physics and generalizes poorly to new scenarios. The candidate will combine physics- and data-driven approaches to gain insights unavailable to either approach alone. This work will serve to enhance our physical understanding of the earth system and to improve predictions of high-impact events in the near and distant future.
We invite applications for 2 PhD positions (m/f/d) in Model-Driven Machine Learning for Climate and Earth Science at the Institute of Coastal Research, Division System Analysis and Modelling with the preferred starting date as soon as possible. The positions are limited to three years.
These positions are designed for curious and driven PhD students (m/f/d) to focus on challenging and impactful research, supported by extensive on-site expertise and top-notch computational resources, with minimal administrative or teaching duties.
- develop hybrid methods that combine deep learning with physical models in a Bayesian framework
- validate new algorithms at using large-scale observational datasets
- absorb and synthesize knowledge from machine learning, numerical modeling and earth science
- write, publish and present your research through scientific journals and conferences
- a master’s degree in mathematics, physics, computer science or the geosciences
- possess strong background and skills in math and programming
- carry out research independently with curiosity, creativity, initiative and persistence
- work well in a small group with diverse skill sets and backgrounds
- learn quickly on-the-job and from research articles on ML, earth and climate science
- communicate well in written and spoken English