PhD Student (m/f/d) in Environmental Engineering

Eawag, the Swiss Federal Institute of Aquatic Science and Technology, is an internationally networked aquatic research institute within the ETH Domain (Swiss Federal Institutes of Technology). Eawag conducts research, education and expert consulting to achieve the dual goals of meeting direct human needs for water and maintaining the function and integrity of aquatic ecosystems.
The Department of Urban Water Management (SWW) has a vacancy for a

PhD Student (m/f/d) in Environmental Engineering
Your research will focus on better understanding stormwater pollution with big wastewater data and machine learning.
Project Background This 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

You will have the chance to explore your own ideas and work closely with other researchers at Eawag, ETH Zurich, and partner institutions. Your work will include publishing your results in scientific journals, presenting at conferences, and support EMPOWER-DD activities. The PhD degree will be awarded by the Swiss Federal Institute of Technology (ETH) Zurich. While this is a research-only position, a maximum of 10% time may be committed to teaching or supporting other activities within Eawag.
Requirements The 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 part of the interdisciplinary project EMPOWER-DD "Effective Wastewater Management through the Integration of Real-Time Population Mobility Data, Extensive Wastewater Archives, and Advanced Data-Driven Modeling". The project brings together expertise in envi-ronmental engineering, data science, urban hydrology and urban infrastructure. It is designed to support climate-resilient and sustainable wastewater management strategies in Switzerland. The research team includes another PhD student working on real-time population mobility data and a PostDoc focusing on integrated modeling of wastewater systems and smart system control. Close collaboration within the team and with external partners is required to ensure scientific impact and practical relevance.
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.
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