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

WIA-Europe; Oberpfaffenhofen; Vor Ort; EUR 50.000 - 70.000; Vollzeit

Zusammenfassung

An aerospace research organization in Oberpfaffenhofen is seeking a PhD candidate to advance climate modeling techniques using machine learning. The role involves developing algorithms, analyzing climate data, and evaluating models against observations. Ideal candidates will have a Master's in a relevant field, strong Python skills, and a passion for climate research. Join an innovative team dedicated to addressing global climate challenges and benefit from a supportive work environment with development opportunities.

Leistungen

Training and development opportunities

Supportive work environment

Promotion of diversity and equality

Qualifikationen

• Completed scientific university education for a PhD thesis in computer science or physics.
• Very good programming skills, especially in Python.
• Experience in data analysis and machine learning.
• Fluency in English (written and spoken).

Aufgaben

• Develop ML methods for climate and Earth system models to improve projections.
• Create innovative ML algorithms for data analysis.
• Evaluate climate models against observational data.

Kenntnisse

Programming skills (Python)

Machine learning

Data analysis

Interest in climate research

Creativity

Ausbildung

Master's or diploma in physics, mathematics, computer science

Jobbeschreibung

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 11,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

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, https://esmvaltool.org/)

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 offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 3267) please contact:

Mierk Schwabe
Tel.:[phone]