24 Doctoral Positions in the International Research Training Group C4LaNd

Job Details

Competition for land - Tackling the climate and biodiversity crises through the food, energy and materials nexus

C4LaNd is an International Research Training Group that addresses a classical “wicked problem”: how to meet the ever-increasing and often conflicting demands on land for food, renewable energy, and materials without compromising biodiversity conservation and climate action. The joint research program offers doctoral positions at the Karlsruhe Institute of Technology (KIT), Hohenheim University (UHO) and the University of Melbourne (UoM) and covers natural sciences, social sciences, and economics to deliver high quality, coordinated research towards land use becoming part of the solution to the climate and biodiversity crises, rather than a major cause.

The qualification program includes training courses in key academic skills, career development, and workshops that foster nexus thinking and peer-to-peer learning. Doctoral students will also complete a one-year research stay abroad in Australia or Germany, enabling them to benefit from the expertise within C4LaNd and will receive a doctoral degree from both institutions. The first cohort of 12 doctoral students in Germany and 12 in Melbourne will begin in late 2026.

Requirements

• Above-average master’s degree or equivalent (either already completed, or completed by the time candidates are selected) related to the position of interest in Economics, Social Sciences or Natural Sciences
• Profound knowledge of research methods relevant to the position of interest
• Delight in engaging within a multi-disciplinary international team
• Fluency in English
• Willingness to undertake a one-year research placement in Australia (UoM) or in Germany (KIT or UHO) and to comply with formalities of a dual degree at both locations

Application Deadline: May 17th 2026

More information, a list of the positions available and how to apply can be found at c4land.earth. For questions on the research projects, please contact the contact person stated in the project descriptions on C4LaNd.earth or contact Dr. Thomas Schmitt (Thomas.schmitt@kit.edu).

Doctoral Positions

Rethinking Food

• PhD1.1 – Food production supply chains in Australia and Germany *(UoM, Australia)*
• PhD1.2 – Scaling up novel land‑use systems *(UHO, Germany)*

Rethinking Urban Spaces

• PhD2.1 – Ethics of care and just mobility transitions *(KIT, Germany)*
• PhD2.2 – Infrastructure deconstruction for regeneration *(UoM, Australia)*
• PhD2.3 – Urban retrofitting and surface‑use conflicts *(KIT, Germany)*
• PhD2.4 – Redistributing mobility space in suburbia *(UoM, Australia)*
• PhD2.5 – Blue‑green infrastructure for climate & biodiversity *(KIT, Germany)*

Rethinking Rural Spaces

• PhD3.1 – Incentives and coordination in land‑use decisions *(UoM, Australia)*
• PhD3.2 – Biodiversity & Indigenous values in energy transitions *(KIT, Germany)*
• PhD3.3 – Trade‑offs of bio‑based products across land uses *(KIT, Germany)*
• PhD3.4 – Participatory governance of nature‑based solutions *(UoM, Australia)*
• PhD3.5 – Roads, Indigenous rights & forest cover (Philippines) *(UoM, Australia)*
• PhD3.6 – Biodiversity impacts of novel land‑use futures *(KIT, Germany)*
• PhD3.7 – Water‑supply optimisation under climate extremes *(UoM, Australia)*
• PhD3.8 – Strategic land buyback for urban flood risk reduction *(UoM, Australia)*

Unlocking Restoration

• PhD4.1 – Forest vulnerability to hotter droughts *(KIT, Germany)*
• PhD4.2 – Spatial optimisation of ecosystem restoration *(UoM, Australia)*
• PhD4.3 – Animals in forest carbon & nutrient cycling *(KIT, Germany)*
• PhD4.4 – Land‑use competition in renewable energy transitions *(UoM, Australia)*
• PhD4.5 – Deep learning for wildfire risk prediction *(KIT, Germany)*
• PhD4.6 – Modelling sustainable European forests *(KIT, Germany)*
• PhD4.7 – Drought adaptation across forest systems *(UoM, Australia)*
• PhD4.8 – Cross‑sensor transfer learning for forest biomass (SAR) *(KIT, Germany)*
• PhD4.9 – Transfer learning for forest structure estimation (SAR) *(UoM, Australia)*