PhD Thesis (f/m/x): Machine Learning for Earth System Models

PhD Thesis (f/m/x): Machine Learning for Earth System Models

Join to apply for the PhD Thesis (f/m/x): Machine Learning for Earth System Models role at German Aerospace Center (DLR)

The Institute of Atmospheric Physics researches the physics and chemistry of the global atmosphere from the ground to an altitude of 120 kilometres.

What To Expect

The Department of Earth System Model Evaluation and Analysis develops innovative methods for evaluating and analysing Earth system models in comparison with observational data with the aim of better understanding and predicting the Earth system. Your PhD will be embedded within an enthusiastic international team focusing on improving climate models and projections with machine learning for actionable climate science and technology assessments.

Your tasks

• Using and further developing ML methods for climate and Earth system models to reduce systematic errors and improve projection capabilities for the atmosphere and/or ocean and sea ice (physics and biogeochemistry).
• Using and developing innovative ML algorithms, e.g. Equation Discovery, Deep Learning, Generative AI, Causal Representation Learning, Uncertainty Quantification, Explainable AI, Foundation Models.
• Evaluation and analysis of climate models in comparison to observations using the Earth System Model Evaluation Tool (ESMValTool,

Your profile

• Completed scientific university education (master or diploma), for example in physics, mathematics, computer science or similar field with adequate educational background for a PhD thesis in computer science or physics.
• Very good programming skills (especially python).
• Experience in data analysis and machine learning.
• Interest in climate research and Earth system modelling.
• Enthusiasm, motivation and creativity.
• Fluency in English (written and spoken).

Remuneration is up to 75 % to the German TVöD 13 depending on qualifications and assigned tasks.

We look forward to getting to know you! If you have any questions about this position (Vacancy-ID 3267) please contact Tel.: .