Earth System Modelling and Machine Learning for Amazon Early Warning Systems

The successful PhD candidate will:

• Develop and apply machine learning methods (e.g. CNNs, hybrid ML-process-based models) for predicting ecosystem disturbances and risks
• Integrate process-based vegetation and Earth system models with data-driven components
• Analyze single and compound hazards affecting Amazon forest resilience
• Contribute to the design and implementation of an early warning platform for scientific and policy-relevant applications
• Publish results in peer-reviewed journals and present them at international conferences
• Collaborate closely with researchers at TUM and partner institutions in Brazil

• A Master's degree in physics, computer science, Earth system sciences, ecology, mathematics, or a related field
• Strong programming skills, preferably in Python (experience with scientific computing, ML frameworks, high performance computing and geospatial data are highly desirable)
• Interest or experience in Earth system modelling, remote sensing, and/or machine learning
• Ability to work independently and collaboratively in an interdisciplinary environment
• High proficiency in written and spoken English
• Willingness to engage in international collaboration and research stays

• The chance to be part of an interdisciplinary collaboration of leading international research institutions
• Participation at international workshops and conferences
• A stimulating working environment in an internationally leading research institution
• A collective pay scheme and associated benefits

Equal Opportunity Statement

TUM is committed to promoting equal opportunities. We explicitly encourage applications from women and underrepresented groups. In cases of equal qualification, women will be given preference within the framework of applicable law. Applications from candidates with disabilities or a migration background are explicitly welcome.

The position is embedded in a close international collaboration between TUM, the newly founded Instituto Relva and other leading Brazilian institutions. The successful applicant will work under the supervision of Prof. Dr. Marina Hirota and Prof. Dr. Niklas Boers. The position is funded for 3 years (PhD, 30h per week). Remuneration is in accordance with the German public tariff scheme (TV-L), salary group E 13.