Scientific programmer (m/f/d)

Organizational unit

Institute of Meteorology and Climate Research Atmospheric Environmental Research (IMKIFU)

Job description

The Global Land-Ecosystem Modelling Group (LEMG) of KIT’s Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMKIFU) is located at the ‘Campus Alpin’ in Garmisch-Partenkirchen, Germany.

We seek an ecosystem modeler to support code development and applications that address interactions of climate change, biodiversity loss and land-use change. This position focuses on the LPJ-GUESS ecosystem model in particular context of LandSyMM (www.landsymm.earth) to enhance its capacity to model important processes relevant within these interactions.

Your tasks include:

• Contribute to the development of a full, online coupling of LandSyMM
• Development of ideas for model applications, simulation set-up and analysis and publications
• Updates and further development of LPJ-GUESS to investigate the impact of land management on ecosystem properties
• Conduct literature searches to obtain data for model testing and evaluation
• Documentation of code development and performance
• Support other team members in questions of code development and application
• Contribute to the group’ overall activities such as project reporting, supervision and teaching
• Collaborate with LEMG team members and partners at other institutions (especially University of Edinburgh and Lund University).

Starting date

01.10.2025

Personal qualification

You have:

• A Ph.D. degree in environmental science, ecology, meteorology, or related areas.
• Demonstrated knowledge in coding languages, ideally C++.
• Demonstrated proficiency in Python or R.
• Fluent written and spoken English and strong communication skills.
• Practical experience with version control systems like git, and familiarity with software testing and documentation practices.
• Familiarity with high-performance computing (HPC) systems, parallel data processing, and data formats such as netCDF.
• Willingness to travel.
• Previous experience in running or developing an ecosystem model is an advantage.