PhD positions in Land surface processes

Ihre Aufgaben

The Max-Planck Caltech Carnegie Columbia (MC³) Center is seeking highly motivated PhD candidates to join our team. Our mission is to integrate advanced Earth observations and machine learning to transform Earth system analysis and predictions for a deeper understanding and sustainable management of our planet - with a focus on land surface processes. We aim to develop new standards in Earth system modelling, foster next-generation scientists, and take on a leading role in Earth system research and education, shaping a sustainable future through innovative, interdisciplinary collaboration within the four partner institutions Max Planck Society, Columbia University (New York), California Institute of Technology, Carnegie Institution for Science.

Land surface processes play a crucial role in controlling water, energy, and carbon fluxes to the atmosphere. These processes are in turn affected by global changes such as rising CO2 and associated climate changes. These changes in land surface processes have a profound impact on the coupling to the atmosphere, triggering feedbacks that resonate throughout the Earth system as a whole. The MC³ Center opens a call for four PhD projects focusing on these diverse aspects of how the land-surface mediates Earth system feedbacks across scales. The unique approach of the MC³ Center combines AI-enabled understanding, modelling, and prediction of land surface processes in the coupled Earth system. We are looking for highly motivated candidates who are keen to work in an international and interdisciplinary environment. The successful candidates will have the opportunity to work on a PhD project within a team with at least two supervisors affiliated to different partner institutions. The candidates will also participate in networking events (e.g. summer schools, interactive online meetings, workshops), conferences and training activities., Work contract for doctoral/PhD candidate (f/m/d) following the collective agreement according to TVöD Bund (E13, 65%, 3 years; in addition, we will provide a pension plan based on the public service). These positions are to be filled as soon as possible.

• Working environment in an international and multidisciplinary team.
• Funded research stay(s) and exchange opportunity with our US partners

Ihr Profil

Successfully completed scientific university studies (Master's degree; or close to completion) in meteorology, environmental sciences, geosciences, computer science, informatics, bioinformatics, physics, or comparable fields.

• Basic understanding of terrestrial biogeochemical cycles, terrestrial ecology, biosphere-climate interactions.
• Practical experience in scientific programming and data processing, as well as in the development and application of numerical models and/or machine learning models.
• Knowledge and experience in the use of numerical and statistical analysis using languages such as Julia, Python and/or similar programming languages
• Experience in the use of HPC clusters, including detailed knowledge of scripting languages for process execution and automation
• Ability to work independently as well as in an interdisciplinary team
• Very good written and spoken English We also welcome applications from candidates who do not meet all requirements.

Kontakt

www.egu.eu, Homepage: https://mc-3.org/open-call, Your application Do you have questions? Stefanie Johnson (sjohnson@bgc-jena.mpg.de) and Dr. Alexander J. Winkler (awinkler@bgc-jena.mpg.de) will be happy to answer any open questions. Are you interested? Use our website to submit your application and to find information on the research projects. Please provide the following documents by January 05, 2025:

• Curriculum Vitae
• Transcript of Records of Bachelor and Master studies
• Contact details of at least two references
• Letter of motivation (max. 2 pages) that addresses the following points:

• What has excited you the most in your research/studies so far?

• Why do you want to pursue your PhD with us, and what are your expectations?

• What key challenges in Earth system sciences and/or Machine Learning do you think exist currently?