PhD Position in Organic Geochemistry: Modern and Microbial Drivers of Biogeoch

Organisation/Company ETH Zürich Research Field Chemistry » Biochemistry Chemistry » Inorganic chemistry Chemistry » Inorganic chemistry Chemistry » Other Environmental science » Earth science Researcher Profile First Stage Researcher (R1) Final date to receive applications 10 May 2026 - 21:59 (UTC) Country Switzerland Type of Contract Temporary Job Status Part-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

PhD Position in Tropical Organic Geochemistry:
Modern and Ancient Microbial Drivers of Biogeochemical Cycling

We seek a highly motivated PhD student for a multidisciplinary project across two groups: (i) The Surface Earth Evolution (SEE) group in the Department of Earth and Planetary Sciences at ETHZ is committed to investigating the processes that drive global climate change over human to geologic timescales. (ii) The Sustainable Agroecosystems (SAE) group in the Department of Environmental Systems Science at ETHZ is dedicated to bringing healthy food from fields to tables around the world for generations to come.

Combined, our work intersects traditional Earth- and environmental-science fields to holistically study the drivers and impacts of changing climate and land use. We effectively integrate field-based sample collection and monitoring with lab-based biogeochemical measurements.

Project background

This position is part of the SNSF-funded project “Constraining tropical wetland greenhouse gas production today and through time: insights from Tumba-Ngiri-Maindombe, Democratic Republic of Congo (Lac Mai)”, which aims to mechanistically constrain the climatic and anthropogenic controls on greenhouse-gas (i.e., carbon dioxide, methane, and nitrous oxide) emissions in Tumba-Ngiri-Maindombe, a severely understudied region that houses the world’s largest tropical wetland. To do so, we will sample greenhouse gases, peatland soils, and lake sediments, and we will analyze these using isotopes, biomarkers, and DNA-based methods.

This PhD student will lead the biomarker component of this interdisciplinary project. Using state-of-the-art organic geochemical techniques, you will identify and reconstruct how biogeochemical cycles and microbial communities have responded to climatic shifts over the past 12,000 years (i.e., the Holocene).

• Perform fieldwork:
  Participate in sampling expeditions to DR Congo to collect high‑resolution, pristine sediment records from Lake Mai Ndombe and surrounding peatlands, ensuring appropriate preservation for organic geochemical analyses
• Describe sediments:
  Build lake sediment‑and peat‑core age models in collaboration with ETH’s cutting‑edge radiocarbon facility . Interpret how sediment sources and accumulation rates have evolved over the Holocene
• Reconstruct ecosystems:
  Use ancient pollen and charcoal signals preserved in sediments to determine how climate and landscape vegetation have evolved over the Holocene
• Analyze biomarkers:
  Extract and quantify sedimentary biomarkers that are diagnostic of specific greenhouse gas‑producing and ‑consuming microorganisms. Reconstruct how greenhouse gas cycling has evolved over the Holocene
• Communicate findings:
  Attend international conferences and workshops and write scientific papers

This position is integral to the research team, which includes 2 PhD students, 2 senior scientists, and several technicians across two research groups. You will therefore lead these tasks but will be supported by all involved PIs and researchers. This multidisciplinary team offers the opportunity to learn, develop, and apply scientific skills beyond traditional organic geochemistry!

Profile

• Education:
  
  MSc degree in geochemistry, geology, or a related field
• Wet‑lab experience:
  Familiarity with geochemical and/or sedimentological techniques (e.g., organic or inorganic isotope analysis); prior experience working with biomarkers or in a similarly controlled laboratory environment is a strong asset
• Computational skills:
  Experience with data reduction and analysis techniques (e.g., statistical methods, numerical models) is highly…