Positions
The Faculty VI (Spatial and Environmental Sciences) of the University of Trier (Biogeography, Prof. Dr.Henrik Krehenwinkel, Research Group Dr. Till-Hendrik Macher), is seeking two PhD positions, commencing on 1 January 2027 and running until 31 December 2029, as part of the interdisciplinary research project ‘BEAM - Bridging Environmental Monitoring and Next-Generation Methods to Rethink Biodiversity Monitoring in a Changing World’ funded by the Carl-Zeiss-Stiftung:
2 PhD positions (m/f/d) - eDNA-based Biodiversity Monitoring
Your responsibilities
Biodiversity is the foundation of stable ecosystems and thus essential for clean water, food security, and human health. As biodiversity declines sharply worldwide, our research group is working within Project BEAM to develop innovative methods for more reliable and comprehensive biodiversity monitoring. The group brings together molecular biology, environmental chemistry, remote sensing, software development, and artificial intelligence, with a focus on environmental DNA (eDNA) analysis - a powerful, non-invasive method that detects numerous species from water or soil samples without directly sampling animals or plants. For the first time, BEAM is applying this method to capture information on environmental stress, population connectivity, and long-term ecosystem change. The project's goal is to fundamentally advance biodiversity monitoring and build the scientific basis for more effective conservation measures and sustainable ecosystem management.
In this position, you will develop innovative eDNA-based monitoring methods as part of one of BEAM's first two work packages:
Develop and validate eDNA-based genetic markers to assess fish population connectivity and gene flow across fragmented and disturbed habitats.
Design intraspecific marker sets able to resolve population-level genetic diversity directly from environmental samples.
Analyse eDNA-derived haplotype data to quantify dispersal patterns and identify barriers to gene flow between fish populations.
Integrate eDNA-based connectivity data with remote sensing and landscape data to model the effects of habitat fragmentation on gene flow.
Develop bioinformatic pipelines for high-throughput, ESV-based analysis of population genetic structure from eDNA datasets.
Develop eDNA-based assays to detect epigenetic markers, such as DNA methylation patterns, indicative of environmental stress in fish species and communities.
Correlate eDNA-derived stress signals with environmental chemistry data to identify the drivers of ecosystem stress.
Establish machine learning models to classify ecosystem stress states from combined eDNA and environmental datasets.
Conduct field and mesocosm validation of epigenetic stress biomarkers against known stressor gradients.
Develop long-term monitoring protocols to track temporal changes in ecosystem stress through repeated eDNA sampling.