The PhD position is part of the DFG funded project “Towards a new, experimentally-grounded model of the evolution of the boron isotope composition of seawater” (PI Dr. Michael Henehan). Boron isotopes are a vital proxy for reconstructing past CO₂ levels and past periods of rapid climate change. However, beyond a few million years ago, applications of the proxy rely on estimates of the boron isotope composition of seawater, which are a considerable source of uncertainty. This PhD project will address this by revisiting (with new experiments) the grounding principles of global boron cycling- in particular the interaction between boron and secondary minerals (clays). With their new experimental data in hand, the PhD candidate will – together with their supervisory team – construct a new numerical model of how the boron isotopic composition of seawater has evolved over the past 100 million years, potentially greatly improving estimates of past pCO₂. The successful applicant will work within a vibrant, international, diverse and multidisciplinary research group (Section 3.3, Earth Surface Geochemistry) at the GFZ, and will receive training in the methodology of boron and lithium isotope analysis within the Helmholtz Laboratory for the Geochemistry of the Earth Surface, a world-class facility equipped with boron-free clean laboratories and multi-collector and single-collector ICPMS instruments for analysis. Funds are also set aside for attendance at the Urbino Summer School in Paleoclimatology in Italy.

Your responsibilities:

• carrying out experiments investigating B and Li isotope fractionation during adsorption on clays
• working efficiently and responsibly in a metal-free clean laboratory environment
• developing and applying numerical box models in collaboration with your supervisory team
• presentation of results at international conferences and in scientific publications

Your qualifications:

• a Master’s degree (or equivalent) in Geosciences, Chemistry or other related sciences
• fluent in spoken and written English
• a positive attitude, excellent problem-solving skills and the ability to work well independently and within a team
• prior experience in clean laboratory work and/or numerical modelling is beneficial but not essential