Master's Thesis
Master's Thesis
Students can find on this page a general overview on preparing a Master's Thesis in the Chairs of Urban Water Management at ETH Zurich: information about the formal requirements, the duration, the assessment procedures and the criteria by which the practical work, the report, the final presentation and the poster will be evaluated and graded. They can also find a list of currently offered topics.
Supervisors can find on this page information on the requirements the students have to fulfil to complete a Master's Thesis in the Chairs of Urban Water Management at ETH Zurich, organizational and didactical information and the templates for the proposal of new Master's Thesis topics.
Information for Students
Are you interested in starting a thesis with the Chairs of Urban Water Management? Please refer to the Checklist for Students and Guidance document provided below. Communicate your intention to the responsible teaching coordinator and contact them for additional information.
Information for Supervisors
Would you like to supervise a thesis for the Chairs of Urban Water Management? Please refer to the Checklist for Supervisors and Guidance document provided below. Use the Thesis project call template to submit a topic to the responsible teaching coordinator. A professor will have to approve the topic before it is uploaded on this page.
Additionally, you are welcome to send us a short slide deck about your proposed topic(s). Our professors can then publicize your thesis topic during their lectures.
Contact
For inquiries or to submit a proposal, please check the Teaching Coordinators webpage and contact the coordinator responsible for Master's Theses.
Currently offered topics
Below you can see the topics currently offered by EAWAG researchers and private partners. If you are interested in one of the offered topics, contact the person indicated. Once you agree you will do your Master's Thesis with them, contact the responsible teaching coordinator to obtain the documentation form.
You can also independently find a supervisor to develop your own topic. In this case, inform from the beginning the responsible teaching coordinator and a potential supervising professor of your intention. Also, pay attention, the professor must agree to a certain topic being the subject of a Master's Thesis. Not all projects are suitable to become Master's Theses. The topics selected below have already received the approval of a professor.
This thesis aims to develop and evaluate data-driven methods for near-real-time performance assessment of bioretention cells, a key type of blue-green infrastructure. Using continuous sensor data on hydrological parameters such as water level, inflow, and soil moisture, the study will analyze system behavior, detect anomalies, and identify performance degradation due to factors like clogging or vegetation changes. By comparing AI-based and traditional statistical approaches, the research seeks to determine effective techniques for early failure detection and maintenance optimization, contributing to more resilient and adaptive urban water management systems. Download Read More (PDF, 641 KB)
Contact: Enrico Bernardini ()
This thesis aims to evaluate the potential of source control and waste design strategies to improve the climate resilience of urban wastewater systems. Using the Fehraltorf catchment’s integrated SIMBA# model, the study will simulate decentralized wastewater storage and controlled release strategies to reduce combined sewer overflow pollution during wet weather and optimize treatment performance during dry periods. By testing probabilistic and rule-based control approaches, the research will assess trade-offs between enhanced treatment stability, reduced overflow emissions, and system adaptability. The findings will contribute to advancing real-time control and smart city approaches for sustainable urban water management. Download Read More. (PDF, 347 KB)
Contact: Jörg Rieckermann ()
This thesis aims to explore how population dynamics influence wastewater pollution by integrating mobile positioning data with high-resolution wastewater quality measurements. Using existing datasets, the study will identify population-related patterns in pollutant concentrations and UV/Vis absorbance spectra, and assess correlations between mobility indicators and pollution levels. Building on these insights, dynamic population information will be incorporated into wastewater generator models to improve pollution predictions compared to traditional static approaches. The goal is to develop a data-driven framework that enhances understanding and forecasting of urban pollution dynamics for more effective water management. Download Read More. (PDF, 398 KB)
Contact: Nicolas Neuenhofer ()
The thesis focuses on validating and analyzing a digital twin (SIMBA# model) of the Worblental wastewater network. The study compares model simulations based on radar precipitation forecasts with real operational data from the wastewater system to assess accuracy and reliability. It involves extensive data analysis, programming (Python/R), and evaluation of forecast performance and model–reality matching over time. The ultimate goal is to improve real-time control and optimization of the wastewater system using predictive modeling. Download Read More. (PDF, 485 KB)
Contact: Max Maurer ()
The goal of this thesis is to perform a comparative analysis of different approaches to identifying priority areas for WASH investments within districts with a high burden of cholera. Specifically, data from a cholera-endemic country (e.g. Democratic Republic of the Congo, Zambia) will be used and priority areas mapped based on (i) epidemiological data only and (ii) various combinations of epidemiological and WASH data. Differences in terms of population and cholera case coverage will be assessed, as well as cost implications. The latter will involve reviewing available tools and methodologies, and developing an appropriate, feasible and reasonably accurate approach for estimating the costs of increasing WASH access in the selected context. Download Read More. (PDF, 288 KB)
Contact: Karin Gallandat ()
The thesis focuses on evaluating how blue-green infrastructure (BGI) and building renewal strategies can reduce stormwater runoff in Zurich under climate change. It addresses the challenge of adapting urban water management to increasing rainfall extremes, growing population density, and infrastructure pressures, while avoiding costly expansion of centralized wastewater systems. BGI offers benefits such as restoring elements of the natural water cycle and mitigating urban heat island effects, but uncertainties remain about its effectiveness under intensified storms. The research aims to explore how different policy levers and renewal rates can influence sewer disconnection, assess whether these measures keep pace with climate change, and determine the thresholds at which centralized expansion may be avoided. Using an existing, calibrated SWMM model for Northern Zurich, the student will evaluate how different development pathways affect combined sewage treatment capacity and surface runoff. Download Read More (PDF, 632 KB).
Contact: Lauren Cook ()
The thesis focuses on identifying optimal compositions of Densified Activated Sludge (DAS) to enhance sedimentation performance in wastewater treatment plants (WWTPs). It addresses the challenge of improving treatment efficiency without expanding infrastructure, as many WWTPs face capacity limits and stricter regulations. DAS, composed of flocs and dense granules, offers better settleability than conventional activated sludge, but the settling mechanisms and ideal size fractions are not fully understood. The research aims to study settling regimes of flocs and granules, assess how different DAS compositions affect settling properties, and examine stratification during settling or feeding phases. Samples will be collected from various Swiss WWTPs and analyzed using experimental methods, with possibilities for advanced techniques like image analysis or settling model development. Download Read More (PDF, 620 KB)
Contact: Mengqi Zhu ()
The thesis focuses on optimizing nutrient removal in Densified Activated Sludge (DAS) systems, which combine flocs and small dense granules. It aims to address the growing need for process intensification in wastewater treatment plants (WWTPs) facing capacity limits and stricter regulations. The research will investigate microbial community distribution in flocs and granules, their roles in nitrification and denitrification, and how DAS composition impacts microbial activities. It will also compare nutrient removal performance between DAS, Conventional Activated Sludge (CAS), and Aerobic Granular Sludge (AGS) systems. Samples will be collected from various Swiss WWTPs, with analyses involving microbial characterization and activity measurements. Depending on interest, advanced methods like bioinformatics or mechanistic modeling could be applied. The ultimate goal is to better understand and optimize DAS configurations for enhanced treatment efficiency. (Download Read more (PDF, 621 KB)).
Contact: Mengqi Zhu ()
The thesis focuses on evaluating the cooling effects of trees under varying soil moisture conditions. Specifically, it seeks to determine if trees provide different thermal comfort outcomes when planted in soils with different moisture levels, as well as to assess the accuracy of the ENVI-met model in simulating tree cooling and energy dynamics. The research will involve modeling the Blue-Green Campus area, collecting meteorological data (e.g., temperature, humidity, and soil moisture), and validating the model with real-world data. The study will also examine the limitations of the ENVI-met model, the sensors used, and the impact of water-sensitive design on soil moisture content and tree cooling. Ultimately, the thesis aims to answer key research questions regarding the relationship between soil moisture, tree cooling, and thermal comfort. Download Read More (PDF, 401 KB)
Contact: João P. Leitão ()
In recent years, wastewater structures in urban drainage systems have been increasingly equipped with sensors with automatic transmission of the measurements to the operators. As a result, an increasing amount of data is now available that provides information about the processes in the drainage system. However, for data analysis and for deriving correct conclusions, the data must be plausible. Since often long periods of time of many different urban drainage structures are used for evaluation, a high number of measurements must be checked for plausibility.
The thesis aims to develop a method using artificial intelligence to automatically check the plausibility of wastewater measurements, minimizing the need for manual visual checks. It will investigate various sensors in different wastewater structures, conducting both measurement-oriented and process-oriented plausibility checks. This ensures accurate evaluation of parameters like combined sewer overflow statistics and dry weather discharge. Download Read More (PDF, 242 KB)
Contact: Juan Pablo Carbajal ()
Wastewater treatment needs to become net-zero by 2050. The potent GHG and ozone depleting substance N2O (265 kgCO2-eq) dominates GHG emissions of wastewater treatment and accounts for around 20% of N2O emissions in Switzerland. To effectively reduce N2O emissions, we need to understand the mechanisms behind its formation which is related to microbial pathways, controlling parameters, process engineering and plant operation. Unfortunately, there is currently no N2O mechanistic model which is able to come up with those cross-plant reduction measures. On the other hand, multi-year N2O monitoring campaigns across different full-scale plants are available, yet their potential to reveal hidden relationships and mechanisms remains unexplored. This thesis aims to use insights from explainable machine learning (XAI) to build a mechanistic model which is able to reduce N2O emissions across different wastewater treatment plants in Switzerland and beyond. Download Read More (PDF, 208 KB)
Contact: Andreas Frömelt ()