Postdoctoral position in "Ecohydraulics and drift in riverine systems

Offer Description

Postdoctoral position in "Ecohydraulics and drift in riverine systems"

The Chair of Groundwater and Hydromechanics at the Institute for Environmental Engineering (IfU), ETH Zürich, is seeking a highly motivated postdoctoral researcher with experience in planning and conducting fieldwork to join an interdisciplinary research project on riverine ecosystems. The position is funded by a Swiss National Science Foundation (SNSF) project led by Dr Luiz G. M. Silva, within the group of Prof.

Roman Stocker, and focuses on the development and deployment of a new‑generation underwater imaging device to transform drift studies in riverine ecosystems. It involves a collaboration with Associate Professor Jenni Raitoharju from the University of Jyväskylä, Finland.

Project background

Many small riverine organisms rely on flow‑mediated processes to complete their life cycles. One such process is drift
, a fundamental mechanism of downstream dispersal for aquatic invertebrates and fish larvae. Although the importance of drift is well established, many of its underlying dynamics—particularly its spatial and temporal variability—remain poorly quantified. This knowledge gap largely stems from limitations in current sampling approaches, which rely primarily on drift nets and are both labour‑intensive and inherently limited in resolution.

Addressing these limitations requires a game change in how drift is observed and quantified. There is a pressing need for non-invasive, cost-effective, and broadly deployable technologies capable of providing long‑term (weeks to years), highly resolved (hourly‑scale) data on species presence, abundance, and movement patterns in rivers and streams. To address this gap, this project aims to realise the Riverine Organism Drift Imager (RODI)—a unique technology developed by our group—as a fully operational, camera‑based underwater imaging system.

RODI is designed to continuously detect, classify, and quantify drifting organisms in situ, thereby enabling drift studies at unprecedented temporal and spatial scales. By bridging engineering, imaging, and aquatic ecology, the project seeks to transform how riverine drift is studied, monitored, and applied in both research and practice.

The primary focus of this postdoctoral position is the application of the Riverine Organism Drift Imager (RODI) to address cutting‑edge scientific questions on macroinvertebrate and fish larval drift
. Research themes include drift dynamics under varying flow regimes, hydropower impacts, mitigation and restoration measures, and drift‑related foraging strategies.

The successful candidate will have the opportunity to work with and further develop a world‑first underwater imaging device
, applying it to innovate drift studies and freshwater monitoring.

Key responsibilities include:

• Deploying, operating, and maintaining RODI at selected field sites in Switzerland and internationally (with a particular focus on Finland).
• Developing and leading novel research ideas that apply RODI to fundamental and applied drift studies.
• Testing, benchmarking, and validating RODI as a drift sampling tool through direct comparison with traditional sampling methods.
• Contributing to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners).
• Designing and implementing an innovative image‑based database to manage and curate data from live organisms sampled with RODI.
• Actively contributing to iterative improvements of RODI, including both hardware and software developments, based on field and analytical feedback.
• Authoring and co‑authoring peer‑reviewed scientific publications and presenting results at international conferences.

Profile

• PhD in Environmental Science, Environmental Engineering, Ecology, Ecohydraulics or a related discipline.
• Demonstrated experience in planning and conducting fieldwork in freshwater ecosystems.
• Strong skills in data management, visualisation and quantitative analysis.
• Experience with statistical modelling; familiarity with or interest in machine‑learning approaches is an advantage, but not a requirement.
• Excellent written and spoken…