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
Master projects:
- Exploring global vs. local learning of products to inform smarter innovation policy at the Energy and Technology Policy Group
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.
Electric mobility in a PED: Charging behavior & vehicle-to-grid flexibility
This project examines how electric-vehicle adoption, charging behaviour and vehicle-to-grid flexibility affect the transformation of Alt-Wiedikon in Zurich into a Positive Energy District by 2050. The student will develop behaviourally grounded hourly profiles of charging demand and vehicle availability and integrate them into the Pol4PED district-scale optimization model. Different adoption pathways and charging modes will be compared in terms of PED attainment, net present cost, peak grid imports, PV self-consumption and investment needs. Particular attention will be given to whether controllable electric vehicles can improve the matching of local generation and demand and reduce the need for stationary batteries. The project will deliver a validated EV module and quantitative evidence on whether mobility electrification creates an additional burden or a source of flexibility for Positive Energy Districts.
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Discover What’s Missing: Mining Environment Assumptions for Robot Manipulation
Robots can learn to perform surprisingly complex tasks, but a learned policy does not necessarily tell us when it will succeed and when it will fail. In this project, you will test a robot manipulation policy in simulation across many different environment variations and investigate how environmental conditions affect its behaviour.
The goal is to discover where the robot can be trusted — for example, which combinations of object properties, friction, placement, or clutter lead to reliable behaviour — and turn these findings into a simple, human-readable description of the robot’s operating conditions. For this, we will use formal languages such as signal temporal logic, which are widely used to establish formal guarantees for intelligent systems.
The project combines robot learning, simulation, and safety, with a strong focus on exploring the environment efficiently and understanding the failures of learned policies.
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Thermal inertia as a flexibility resource in district heating and cooling networks
Buildings account for around 40% of total energy consumption in Europe, of which an estimated
80% is used for heating and cooling. District Heating and Cooling (DHC) networks are an
increasingly used technology to satisfy the thermal demands of buildings in densely populated
areas, as they facilitate the cost-effective use of renewable energy resources and waste heat.
Flexibility in DHC networks is crucial for optimizing operations in terms of cost, emissions, and peak load reduction.
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Control Oriented Meta-Learning for HVAC Systems with Embedded Priors
In recent times, the field of Heating, Ventilation, and Air Conditioning (HVAC) has seen a shift from classical feedback control to more efficient, data-driven solutions. With the ambition of improving energy efficiency, saving time, and developing flexible cross-domain solutions.
This project aims to study the correlation between priors, which represent domain knowledge of the underlying physical process, and recent algorithms from the fields of Machine-Learning and Meta-Learning to develop optimal and constraint-aware system parametrization and control strategies.
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Self-Learning Ovens
Household ovens are nonlinear thermal systems whose behaviour changes with insulation aging, heating-element wear, sensor drift, food load, and ambient conditions. A model calibrated once during manufacturing can therefore become inaccurate, reducing temperature performance and increasing energy use. This thesis will develop a physics-guided learning and control framework that combines a thermodynamic digital twin, online system identification, anomaly detection, and safe or model-based reinforcement learning (RL). The main objective is to detect changes in the oven, update the model, and optimize heating strategies without violating operational constraints. As a small optional component, a large language model (LLM) may assist engineers in extracting information from maintenance notes and technical documents, proposing physically meaningful features, or explaining anomalies already detected by dedicated time-series methods. The LLM will not control the oven or replace the diagnostic algorithms.
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Decentralized Grid-Equivalent Model Identification
With the increase in sustainability concerns and ”net-zero” initiatives, the power grid is gradually being
replaced with renewable energy sources and power electronic technologies (PETs). However, the
integration of PETs into the grid comes with several challenges, in particular, with data-driven modeling,
control, and stability analysis of the grid. To this end, grid impedance models have been very useful, for
example, aiding in ascertaining small-signal stability, enabling adaptive control to ensure stability, and
ensuring the safety of distributed generation systems by identifying islanding conditions.
To identify the grid impedance, we normally consider a ”small-signal model”, i.e., a linearized
transfer function that represents the equivalent impedance relating the terminal voltages as inputs and
current injected at points of common coupling (PCCs) as outputs. For a large-scale power grid, the
identification via a centralized approach presents many challenges: synchronizing time steps owing to
different operating time-scales of various sources and loads, and of course scalability.
Thus, in this project, we would like to develop a decentralized grid-equivalent identification algorithm
in the time domain.
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Physics-Consistent Lifelong Learning and Adaptive Control for Energy-Efficient Ovens
Modern household ovens are expected to provide high cooking performance while minimizing energy consumption throughout their lifetime. However, the thermal characteristics of an oven gradually change due to aging effects such as insulation degradation, heating element wear, and sensor drift, causing conventional factory-calibrated models and controllers to lose accuracy over time. This thesis investigates a novel lifelong learning framework that enables ovens to continuously adapt to these changes by combining physics-consistent machine learning with adaptive control techniques. The project will develop thermodynamic models that incorporate known physical principles, employ Bayesian optimization for automatic calibration, and use online system identification to continuously update the model using operational data. The learned models will then be integrated into adaptive control algorithms that automatically adjust controller parameters to maintain temperature regulation accuracy while improving long-term energy efficiency. The proposed methods will be validated using simulation, industrial datasets, and available hardware at V-ZUG, with the possibility of deployment on a real oven. This project provides an opportunity to work at the intersection of control theory, machine learning, system identification, and industrial automation on a real-world problem with significant practical impact.
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Safe and adaptive learning-based MPC: is it even possible?
Model Predictive Control (MPC) excels at handling complex constraints, but its real-world success relies heavily on highly accurate system models and meticulously tuned cost functions. While learning-based MPC addresses this by adapting to changing environments online, it notoriously lacks the reliability required for practical deployment, risking system instability. This project aims to bridge the gap between adaptability and safety by leveraging differentiable MPC, a cutting-edge framework that treats optimization solvers as differentiable functions. By utilizing gradient-based techniques to tune cost functions and constraints, you will develop a control scheme that learns dynamically while providing rigorous safety guarantees.
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Evaluating Incentive-Driven Demand Response for Residents
Residential occupants shape both how much energy households use and when the grid is stressed. Demand-side management lets households shift flexible activities (showering, laundry, dishwashing) into windows of high renewable availability, cutting peaks, costs, and CO₂. But residents don't respond to grid signals on their own: it depends on how clearly information reaches them, how easy the action is, and whether it's rewarded. This thesis investigates how day-ahead building-simulation results can be turned into concrete, well-timed recommendations for residents at Empa's NEST living-lab, and whether modest incentives produce a measurable, lasting shift in behaviour.
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Dynamic Performance Certification of Grid-Forming Converters Under Large Grid Disturbances
As renewable energy penetration increases, power grids are becoming weaker and less voltage-stiff, creating new stability challenges for grid-connected converters. Grid-forming converters offer a promising solution by supporting voltage, frequency, inertia, and fault response during major grid disturbances. However, their ability to deliver these services quickly and reliably depends strongly on converter control settings and grid conditions. This thesis will investigate the large-disturbance dynamic performance of grid-forming converters beyond conventional case-by-case simulations and simplified voltage-source models. The student will develop analytical performance indicators that capture the influence of individual control loops, grid strength, and key control parameters. Particular attention will be given to the tradeoff between fast dynamic response and system stability. The proposed analytical certification method will be validated in MATLAB/Simulink and applied to the design of improved grid-forming control strategies. The project offers an opportunity to work on a highly relevant topic in renewable-energy integration, combining power-system theory, converter control, analytical modelling, and simulation.
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Image-based prediction of material constructions for urban building energy simulation
Urban Building Energy Models (UBEMs) often require detailed inputs on building envelopes to produce accurate outputs. However, this information is typically neither stored nor readily accessible. This thesis aims to augment energy models with material constructions predicted from visual data and building register data using deep learning techniques. The project will focus on a case study in Zurich in collaboration with the City of Zurich.
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Image-based prediction of window-to-wall ratio for urban building energy modeling
Urban Building Energy Models (UBEMs) often require detailed inputs on building envelopes to produce accurate outputs. However, this information is typically neither stored nor readily accessible. This thesis aims to augment energy models with WWR information obtained from visual data using deep learning techniques. The project will focus on a case study in Zurich in collaboration with the City of Zurich.
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Self-consuming Generative Models: a Gaussian Analysis
Modern generative models are increasingly trained on web-scale datasets that contain content produced by earlier models. When this process is repeated over several generations, errors in the synthetic component can accumulate and the learned distribution can drift away from the original data distribution. This phenomenon, known as model collapse, can appear as bias in the mean, shrinking variance, or loss of low-probability modes. This thesis studies a concrete intervention: at each training generation, a curator may inject a limited amount of verified real data. The goal is to decide when and how much real data to inject so that model drift remains small over a finite horizon while respecting a data-acquisition budget. The project combines stochastic optimal control, optimal transport, probability, and numerical experimentation.
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Research Internship - Machine learning and digital twins
Our research group works on increasing the efficiency of robotics systems in real-world deployment by
enabling quick data collection, calibration, and policy training while ensuring safety and efficiency. For
this, we develop novel learning-based control and policy optimization techniques.
We're looking for a skilled machine learning (ML) engineer to develop cutting-edge AI algorithms for
digital twin applications in real-world industrial systems. The digital twin applications will include process
monitoring, estimation, analysis, optimization, and control policy development. The project requires
hands-on expertise in ML, time-series forecasting, modelling, and control of industrial plants.
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Thermal and Structural Analysis of 3DP Building Components
Additive manufacturing has shown great potential in the building industry, enabling the fabrication of complex, customisable geometries that were previously impossible with conventional fabrication. Combining the advancement of this technology with ceramics, a fundamental building material with exceptional properties of durability, thermal stability and strength, functional properties of building components can be enhanced. This research investigates the potential of optimising the thermal and structural properties of ceramic 3D-printed components through a parametric design workflow that explores parameters of form, thickness and infill geometry.
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Organic 3D-Printable Paste Development for Ceramic DIW
Clay can be shaped and formed through a wide range of techniques, and a subsequent sintering process transforms it into ceramic, a stiff and durable material well-suited to building components. In Direct Ink Writing (DIW), clay must retain a certain water content to enable extrudability for printing. After printing, the part passes through a drying stage in which water is lost, causing shrinkage, and is then sintered at high temperatures. The shrinkage and warping that occur during drying and sintering introduce dimensional deviations, which are problematic for the tolerances required in building components. Therefore, this research develops an organic composite paste to be co-extruded during the 3D-printing process, which aims to reduce the drying and sintering shrinkage of the resultant ceramic component.
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Automatic Calibration Procedure for a 5-Axis Robotic 3D Printer
Additive manufacturing systems are typically operated in an open-loop fashion, where both motion and material extrusion are precomputed offline. This makes the process sensitive to disturbances such as geometric misalignments, material variations, and changes in process conditions. Accurate geometric calibration, such as bed leveling and multi-axis alignment, is therefore an important prerequisite for reliable and repeatable printing. This is especially relevant for 5-axis additive manufacturing systems, where additional rotational axes introduce further calibration challenges. This project focuses on improving the reliability of a custom-built 5-axis 3D printer at IfA by developing automated calibration and startup procedures.
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Characterisation of past vertical extension projects in Zurich
To achieve ecological sustainability, urban densification must shift from carbon-intensive demolition toward alternative methods, such as vertical extensions. However, the lack of systematic mapping of past projects hinders evidence-based scaling-up of this approach. This thesis aims to bridge this gap by developing a data-driven pipeline to identify, characterise, and evaluate past projects. Using computational tools, the workflow integrates open data and satellite imagery to extract features of past projects. The resulting structured, geocoded database and quantitative and qualitative assessment framework will ultimately facilitate a rigorous analysis of implementation potential and the strategic scaling-up of urban densification practices.
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Evaluating the use of thermal indicators for indoor heat stress assessment
As heat events intensify and become more frequent, concerns about the health impacts of indoor heat stress are growing. Yet, most indoor overheating studies still rely on dry-bulb temperature or comfort indices, with no consensus on which indicators are appropriate and health-relevant in buildings. This project evaluates a small set of candidate indicators for typical Swiss apartments and offices by combining building simulations with selected thermophysiological model outputs.
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Feedback control for the first Swiss local energy markets
How can one safely control an electricity grid with multiple selfish stakeholders such as electric vehicles owners and solar panel owners, in real time? This project investigates this question for Walenstadt, a Swiss town where "the grid of tomorrow" is currently being created. The goal is to test PRIME, a recently proposed feedback market mechanism that controls the grid by providing economic incentives to the stakeholders, to drive their decision-making and achieve coordination. The tests are performed in simulation on a realistic model, but tests in the real grid of Walenstadt are a possibility when the simulations are successful.
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Conceptual design of decentralised data centres in various urban building energy systems
The master’s thesis will conceptually design how decentralised data centre can be integrated and utilised within different types of buildings. There is a strong emphasis on achieving a technical solution that is both redundant and sustainable. Different types of buildings with different energy demands will be modelled to outline the integration of data centres.
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