Open Master's Thesis Positions

On this page you will find a selection of possible Master Thesis opportunities, some notified to us directly by the research groups of MEST Tutors and some listed on the SiROP database.

This list is not exhaustive, other Thesis projects might exist, please check the respective listings of Departments and research groups you are particularly interested in.

See also Internship opportunities.

Projects directly supplied by MEST Tutors

Projects from the SiROP Database

ETH Zurich uses SiROP to publish and search scientific projects. Here is a selection of projects currently available which may be suitable for MEST students. For more information visit external page sirop.org.

Chair of Architecture and Building Systems

VENTILATION DESIGN FOR BIPV FACADES

Using physical experimentation, explore ventilation cavity design for a BIPV facade. Show details 

Chair of Architecture and Building Systems

BUILDING PERFORMANCE SIMULATION OF HISTORIC BUILDINGS

Energy simulation of historic buildings using EnergyPlus and WUFI Pro. Comparing results to a calibration dataset and establishing best practices for simulating historic buildings. Possible to extend into a Master Thesis, focusing on climate adaptation for historic buildings. Show details 

Automatic Control Laboratory

AI-based optimization of wet-milling processes

Wet-milling is a critical process in various industrial sectors, where the grinding efficiency significantly influences both economic and environmental performance. This thesis project aims to optimize wet-milling operations by leveraging artificial intelligence, specifically Bayesian optimization and neural networks, to determine optimal process parameters. The work will begin with a comprehensive system identification phase, modeling the nonlinear dynamics of the milling process while accounting for varying feed materials and bead characteristics. Subsequently, a data-driven optimization pipeline will be developed and validated to enhance operational efficiency. This interdisciplinary project combines control theory, machine learning, and process engineering, with potential contributions to academic publications and real-world industrial applications. Show details 

Automatic Control Laboratory

Advanced Volume Control for Pipetting

Improving volume control precision and robustness in automated pipetting remains a challenge, often limited by traditional indirect methods. This project explores direct volume control by leveraging internal air pressure measurements and the ideal gas law. Key obstacles include friction, pressure oscillations, varying liquid viscosities, evaporation, and liquid retention. Collaborating with Hamilton Robotics, the goal is to develop a robust control architecture for their precision pipette (MagPip) suitable for diverse liquids. The approach involves mathematical modeling based on sensor data, designing robust control strategies to handle nonlinearities and disturbances, and validating through simulation and real-world experiments. Show details 

Automatic Control Laboratory

Games in Motion: Learning Equilibria in Metric Spaces

Imagine a strategic competition among multiple decision-makers in a broad scale. These can be a Democrat and a Republican competing for votes across a large population, or Pepsi and Cola battling for market shares in a vast region. What are the possible outcomes? How can one gain an edge compared to the opponent? These interactions can be characterized as equilibrium-seeking problems in metric probability spaces, featuring strategic decision-making under evolving distribution dynamics. We will bridge insights from game theory, dynamical systems, and optimal transport to shed light on solution concepts, algorithmic pipelines, and performance guarantees in such non-stationary environments. Show details 

Automatic Control Laboratory

Safe and Reliable Model Predictive Control using Differentiable Optimization

Safety violations in control systems can lead to catastrophic outcomes, from autonomous vehicle crashes to power grid failures. While Model Predictive Control (MPC) offers powerful safety mechanisms through constraint enforcement, a critical dilemma emerges: improved controller performance often comes at the expense of safety margins. Traditional tuning approaches that prioritize performance metrics may inadvertently compromise safety guarantees. This project addresses this fundamental challenge by developing a tuning framework that enhances MPC performance while providing anytime safety guarantees—ensuring the system remains safe even during ongoing optimization. The approach offers a principled solution for deploying high-performance, safety-critical control in autonomous systems, robotics, and industrial processes. Show details 

Automatic Control Laboratory

Adaptive control via reinforcement learning: stability, optimality, and robustness

This project explores reinforcement learning (RL) for adaptive control of linear time-invariant systems, with a focus on achieving stability, optimality, and robustness. While RL-based adaptive control methods are gaining popularity, most lack rigorous stability guarantees, especially when applied to the linear quadratic regulator (LQR) problem. Building on recent advances in sequential stability analysis, the project aims to develop RL algorithms that ensure closed-loop stability and convergence to the optimal LQR policy. Theoretical insights will be validated through simulations on representative control systems. Show details 

Chair of Architecture and Building Systems

Developing Extreme Weather Files for Resilience-Oriented Building Simulation in Zurich

As climate extremes intensify, Typical Meteorological Year (TMY) weather files are increasingly insufficient to assess buildings. Show details 

Chair of Architecture and Building Systems

Evaluating the Impact of Facade Wall Assemblies on Thermal Comfort using a Solar Simulator

Rapid urbanization has intensified the Urban Heat Island (UHI) effect in many cities worldwide, leading to higher ambient temperatures and reduced thermal comfort. Building wall assemblies affect how heat is absorbed and re-emitted into the surrounding environment. To better understand and mitigate these effects, this master’s thesis will investigate the thermal and radiative behavior of selected façade wall assemblies under controlled “sunlight” conditions using the Solar Simulator at the Zero Carbon Building Systems (ZCBS) Lab. Show details 

Urban Energy Systems

Data-Driven Demand-Side Flexibility Quantification

The integration of distributed renewable energy sources into electric power grids is essential for transitioning to low-carbon energy systems. However, the intermittent nature of distributed renewable energy poses challenges to grid stability. Demand-side flexibility has emerged as a key solution, allowing consumers to adjust their electricity usage to help balance supply and demand. Buildings, as major energy consumers, offer substantial demand-side flexibility potential by shifting or reducing their energy use without compromising occupant comfort. To harness this potential, predictive energy management systems have been developed to optimize energy usage and quantify flexibility, typically represented as flexibility envelopes. These envelopes are used by distribution system operators (DSOs) for effective grid coordination. However, existing methods for flexibility quantification are largely optimization-based, requiring significant computational resources—especially problematic for real-time or rolling updates, which involve repeatedly solving complex models under varying conditions. This limits their scalability and responsiveness in practice. This research aims to develop a machine learning-based approach to predict flexibility envelopes using historical data. The goal is to provide real-time flexibility estimates with significantly reduced computational cost, making this method more practical for integration into smart energy systems. Show details 

Chair of Architecture and Building Systems

Vertical Extensions: A Technological-Ecological Analysis through archetyping

Urban densification in cities like Zurich necessitates sustainable strategies that address environmental, social, and economic priorities. Vertical extensions—adding floors to existing buildings—offer a viable solution to increase housing capacity while minimizing land use and preserving the urban fabric. However, implementation is often hindered by regulatory, technical, and socio-economic challenges. This project focuses on analyzing vertical extensions through an archetyping approach, with an emphasis on identifying key technological and ecological parameters that influence project success. By examining case studies, collecting data, and conducting statistical analyses, the research seeks to uncover correlations between these parameters and project outcomes. The findings aim to inform future sustainable densification strategies and guide decision-making. Show details