PhD Student (m/f/d) in Environmental Engineering
The Department of Urban Water Management (SWW) has a vacancy for aYour research will focus on better understanding stormwater pollution with big wastewater data and machine learning.
Project BackgroundThis position is embedded in the SNSF-funded research project EMPOWER-DD, which aims to advance climate-smart wastewater management using real-time data and predictive modeling. The PhD position focuses on exploiting Switzerland’s extensive monitoring archives to quantify stormwater runoff contributions to urban water pollution. Climate change and urbanization are intensifying the challenges of managing stormwater pollution in cities. In this context, understanding how rainfall and catchment characteristics impact the concentration and composition of pollutants in stormwater runoff is critical. This PhD project offers a unique opportunity to address this issue curating and analyzing a large-scale dataset spanning over dozens of wastewater treatment plants and environmental variables across Switzerland.
In this doctoral research project, we are looking for a motivated PhD student to work on the following tasks:
- Compile and harmonize a national-scale dataset combining 20+ years of WWTP influent/effluent data with Swiss authorities and utilities and augment it with high-resolution data on meteorology, land-use and catchment topology
- Conduct exploratory analysis of the data set
- Develop and validate machine learning models (e.g., Random Forest, autoencoders) to predict stormwater pollution across diverse catchments
- Benchmark Swiss data against available information, e.g. from DWA or the U.S. National Stormwater Quality Database, and assess the transferability of predictive relationships
RequirementsThe successful candidate will have:
- a MSc degree in data science, physics, environmental engineering, hydrology, civil or mechanical engineering or a related field
- a solid foundation in data analysis, machine learning and time series analysis, ideally with large and heterogeneous environmental datasets. We welcome candidates who are eager to deepen their expertise in these areas
- excellent knowledge of writing skills in English and strong communication skills in English, German and/or French and a collaborative mindset.
- solid programming skills in Python, R, or similar languages
- interest in urban water systems, stormwater pollution, and the impacts of climate change on infrastructure
- the capability to think critically,work independently and a pronounced sense of reliability and self-organization
You will be supervised by Dr. Jörg Rieckermann (SWW), with co-supervision and mentoring from Dr. Carlo Albert (Applied Systems Analysis), and support from project partners Dr. Lena Mutzner (stormwater quality). Prof. Dr. Max Maurer (ETH Zurich) will serve as your PhD thesis advisor.
Eawag is a modern employer and offers an excellent working environment where staff can contribute their strengths, experience, and ways of thinking. We promote gender equality and are committed to staff diversity and inclusion. The compatibility of career and family is of central importance to us. For more information about Eawag and our work conditions please consult www.eawag.ch and www.eawag.ch/en/aboutus/working/employment.
Please submit your application including a CV, a brief statement about your motivation for this position and how your skills and experience support curation and mining of big wastewater data, copies of your academic qualifications and names of potential referees.
The deadline for applications is 15 August, 2025 or until the position is filled. The successful candidates should ideally start in Fall 2025.
For further information, please contact: Dr. Jörg Rieckermann, joerg.rieckermann@eawag.chNicolas Neuenhofer, nicolas.neuenhofer@eawag.ch
We look forward to receiving your application. Please send it through this webpage, any other way of applying will not be considered. A click on the link below will take you directly to the application form.
Eawag: Swiss Federal Institute of Aquatic Science and Technology