Open Master's Thesis Positions

**Background:** The transition to renewable energy and the increasing frequency of extreme cli-mate events challenges the resilience of residential energy systems. Assessing how different building types, heating systems, and flexibility assets (such as solar PV panels and electric vehicles) respond to extreme scenarios is crucial for ensuring energy security, minimizing emissions, and maintaining occupant comfort and affordability. A systematic, simulation-based resilience assessment framework integrating building models and Python-based analytics can provide actionable insights for energy system planning and policy. **Project Tasks:** 1) Framework Development: The idea here would be to extend an existing framework consisting of a group of residential houses with heterogeneous archetypes (including heat pumps, boilers, EVs, storages, PV, and others). The extension of the framework would mostly be focused on more accurate reflection of the U-values and building properties based on 9 different building archetype definitions for SFH and MFH. In addition, the modelling of the extreme scenarios (heat waves, cold spells, financial crisis) would be an addition to this framework. The information for the extreme scenario modelling would be obtained from the past semester project that investigated the defining and assessment of heat wave and cold spell scenarios in Switzerland. 2) Scenario Analysis: The goal here would be to run simulations for various combinations of building types, heating systems, flexibility assets, and extreme scenarios. Additionally, to be able to compare and quantify the resilience of different configurations, the framework should provide outputs for energy use, emissions, thermal comfort, energy cost, and flexibility. 3) Performance Assessment: Analyze and visualize the results by comparing the resilience of different configurations under extreme conditions. Potentially it would be interesting also to identify trade-offs and synergies between energy performance, comfort, cost, and flexibility. 4) Reporting: Final part of the project would focus on documenting methods, results, and key findings.

1) Extend the existing framework to integrate building archetypes, heating systems, extreme scenarios, and flexibility assets. 2) Quantify the impacts of these factors on key performance indicators: energy use, emissions, thermal comfort, energy cost, and system flexibility. 3) Demonstrate the framework on representative Swiss residential cases.

Arnab Chatterjee (arnab.chatterjee@empa.ch)

_Discaimer_: please download the attached PDF for a better reading experience. Model predictive control (MPC) is a powerful model-based control technique that received significant attention both in industry and academia due to its ability to efficiently obtain control inputs that are explicitly optimized for a predefined objective and fulfill process constraints. At each time-step, the MPC controller utilizes the measured (or estimated) value of the system state to construct a future prediction of the state and input trajectory. Then, by solving an optimization problem, the future predicted trajectory is chosen to minimize a given objective while fulfilling constraints. Each predicted trajectory has finite length to ensure that the problem can be solved in real time, which can potentially lead to myopic planning and reduce the closed-loop performance of the controller. A well-established way to overcome this inherent suboptimality, is to approximate the infinite-horizon cost by including a _terminal cost_ in the MPC problem, as seen in Equation (1) in the pdf. The terminal cost, i.e., a cost applied to the last state of the predicted trajectory, is usually designed to approximate the _value function_ of the terminal state, that is, to approximate the total cost-to-go that one has to pay to steer the terminal state to the target state. The value function can be learned efficiently in many situations. Observe in Figure 1 of the PDF, for example, how an MPC equipped with a value function is able to outperform a nominal MPC in the pendulum swing-up task, in the same simulation settings. Despite the great performance benefits that a value function used as terminal cost can bring, this approach does not work well if the system dynamics changes over time. In this case, to retain satisfactory performance, the value function needs to be re-learned, resulting in significant computational effort. In this project, we wish to alleviate the computational effort required for re-learning by utilizing _meta-learning_, a novel strategy for effectively adapting controllers to various environments and reducing the amount of data needed for the training.

This project will focus on establishing a meta-learning algorithm that can quickly adapt the value function associated to an MPC controller if the environment dynamics change. To achieve this, the study will first focus on constructing a value function, for example following the method in [1]. Subsequently, the meta-learning algorithm will be adapt the value function to different dynamics. If time permits, the project could also address this problem from a theoretical perspective, for example by establishing guaranteed suboptimality bounds on the resulting MPC controller. All results will be tested in simulation. The goals of the project are as follows: 1. familiarize with the algorithms described in [1]; 2. construct and deploy the meta learning scheme; 3. establish theoretical properties of the scheme. **References** [1] Ugo Rosolia and Francesco Borrelli. Learning model predictive control for iterative tasks: A computationally efficient approach for linear system. IFAC-PapersOnLine, 50(1):3142–3147, 2017.

We are looking for motivated students with some prior knowledge about MPC, optimization (e.g. gradient descent) and Python. Please send your resume/CV (including lists of relevant projects/courses) and transcript of records in PDF format via email to rzuliani@ethz.ch.

_Note_: please read the attached PDF for a better reading experience. Safety is a critical concern in control applications, particularly in systems where constraint violations can lead to hazardous outcomes, such as in autonomous driving, robotics, or process control. Model Predictive Control (MPC) offers a powerful framework to ensure safety by explicitly accounting for system constraints over a receding prediction horizon. By leveraging an internal model of the system and repeatedly solving a constrained optimization problem online, MPC can proactively enforce safety limits on states and inputs, while anticipating future behavior. This predictive capability enables MPC to integrate safety requirements into the control synthesis process, making it a suitable tool for safety-critical applications, as highlighted in recent works emphasizing formal guarantees and robust constraint satisfaction under uncertainty. MPC performance can be significantly enhanced through hyperparameter optimization techniques, which systematically tune parameters such as constraints and cost weights to better suit specific control tasks. As demonstrated in recent work [1], such optimization can lead to improved closed-loop performance by balancing control accuracy and computational complexity. These methods move beyond manual trial-and-error tuning, offering scalable and automated strategies to deploy high-performance MPC in real-world applications. While hyperparameter optimization can significantly enhance the performance of MPC controllers, it may also introduce safety risks if not carefully constrained. Automated tuning methods often focus on improving performance metrics such as tracking accuracy or economic cost, potentially overlooking critical safety constraints or robustness margins. For instance, optimizing cost weights without explicitly accounting for constraint satisfaction can result in aggressive control actions that violate system limits or reduce stability margins (see the example in Figure 2). Therefore, it is crucial to integrate safety-aware mechanisms into the optimization process, to ensure that performance improvements do not come at the expense of system safety.

The objective of this project is to develop a tuning framework that enhances the performance of a Model Predictive Control (MPC) scheme while providing _anytime_ safety guarantees. That is, the controller must ensure that the closed-loop system remains within predefined safety constraints at all times, even if the optimization process is interrupted before convergence. To achieve this, the project will build on recent advances in closed-loop optimization methods [1], [2], which leverage the concept of _differentiable optimization_—the ability to compute derivatives of the solution maps of parametric optimization problems. These methods open new possibilities for systematically tuning the parameters of an MPC controller by incorporating gradient-based optimization techniques. Although the project is primarily theoretical, it will also include a simulation component to demonstrate and validate the proposed methods. The main tasks of the project are: 1. Study and understand the closed-loop optimization techniques presented in [1] and [2]; 2. Design a differentiable tuning scheme that guarantees anytime feasibility and safety of the MPC controller; 3. Implement the proposed scheme and validate its effectiveness through numerical simulations. **References** [1] R. Zuliani, E. C. Balta, and J. Lygeros. BP-MPC: Optimizing the closed-loop performance of MPC using BackPropagation. IEEE Transactions on Automatic Control, 2025. [2] R. Zuliani, E. C. Balta, and J. Lygeros. Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees. arXiv preprint arXiv:2403.04655, 2024.

We are looking for motivated students with some prior knowledge about MPC, optimization and Python. Please send your resume/CV (including lists of relevant projects/courses) and transcript of records in PDF format via email to rzuliani@ethz.ch.

Adaptive control seeks to maintain desired system performance despite uncertainties or changing dynamics, making it essential in applications like robotics, aerospace, and power systems. Reinforcement learning (RL), particularly in the form of adaptive dynamic programming, has recently emerged as a promising approach to design adaptive controllers directly from data, bypassing the need for explicit system identification. At the heart of this challenge lies the linear quadratic regulator (LQR) problem, a benchmark task where the goal is to achieve both closed-loop stability and optimality by ensuring the adaptive controller converges to the optimal LQR policy. However, most existing RL-based adaptive control methods lack rigorous guarantees on stability, which limits their safe application in real-world systems. Moreover, questions of robustness under noise, disturbances, or model uncertainty remain largely open. This project aims to address these gaps by leveraging sequential stability analysis techniques to develop RL-based adaptive control methods with formal guarantees on stability, optimality, and robustness for linear time-invariant systems. The student will combine theoretical tools with practical algorithm development and will validate the proposed methods through detailed simulations on representative systems, ensuring both theoretical soundness and practical performance.

The student will: Learn foundational concepts, including adaptive control, reinforcement learning, and adaptive dynamic programming for LTI systems; Review and synthesize the current literature on RL for adaptive control, focusing on identifying gaps in stability guarantees; Develop and implement algorithms that integrate sequential stability analysis with RL-based adaptive control; Bring together the theory, and analyze the results; Promising results could be turned into a publication.

Dr. Feiran Zhao and Marcell Bartos, zhaofe@ethz.ch, mbartos@ethz.ch.

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In this project, up to three (or two for a semester project) façade wall assemblies will be evaluated in collaboration with A/S researchers and tested under the controlled conditions of the ZCBS Lab’s Solar Simulator. A sensor array, including globe thermometers, infrared cameras, and thermocouples, will measure parameters such as surface temperature, mean radiant temperature, and ambient air temperature. Multiple measurement campaigns will capture data at standardized irradiance levels. The resulting data sets will be statistically analyzed (e.g., using Python, R, etc.) and compared with existing literature. Expected outcomes by the end of this project: - Participation in the design of the wall assemblies and calibration of the measurement setup - A structured dataset of monitored environmental parameters (surface temperatures, MRT, ambient conditions) for each test wall assembly - Statistical and comparative analysis of the data, quantifying each assembly’s potential impact on outdoor thermal comfort - Recommendations on design or assembly strategies to improve outdoor comfort, as well as guidelines for future experimental work - A final report documenting the research process and findings (thesis/report).

The primary goal of this thesis is to evaluate the performance of up to three different wall assemblies based on their impact on near-wall thermal comfort. Data will be generated through experimental measurements using the Solar Simulator in the ZCBS Lab: - Explore up to three façade wall assemblies for testing under the Solar Simulator in collaboration with A/S researchers and technicians. - Plan and integrate a comprehensive sensor array (e.g., globe thermometers, infrared cameras) to capture key thermal parameters. - Conduct a series of controlled tests to assess each assembly’s performance under standardized irradiance levels. - Statistically analyze the collected data and benchmark findings against existing literature.

Please send your CV and transcript to duran@arch.ethz.ch, and feel free to ask if you have any questions.

The Innosuisse Flagship Project ‘Think Earth’ aims to demonstrate a fundamental transformation process towards climate-neutral construction with timber and earth. With ‘Suproject 9: Case Studies & Integration’, we aim to advance the formalization and demonstration of an integrated design process that seamlessly incorporates earth and wood into architectural designs. To expand our understanding of the interaction between materials, people, and the environment, we investigate the use of local potentials in combination with earth/timber (e.g. use of passive solar gains), the influence of hybrid earth/timber elements on comfort (e.g. temperature sensation, humidity and acoustics) and the influence of earth/timber buildings on the environment (e.g. via the balancing of greenhouse gas emissions during construction and operation). Together with partners from academia and industry, we work in an interdisciplinary way to integrate the two materials in different case studies. At the same time, we support our partners so that design objectives are achieved and validated during and after implementation. The thesis is based on the Humanitas Case Study, a mixed-use building in Acquarossa, Ticino, which integrates earth and timber components alongside passive design strategies. The building is currently in the detailed design phase, leaving room for design refinements—such as adjustments to window-to-wall ratios (WWR, or the material distribution of earth and wood. The primary objectives of the study are: - To evaluate the current design’s thermal and environmental performance. - To explore potential design improvements through simulation-driven analysis. - To derive actionable design guidelines—or rules of thumb—for hybrid earth/timber construction in similar Alpine and temperate regions. The methodology will include the following phases: - **Digital Modeling and Simulation**: Performance analysis using tools such as WUFI, Climate Studio, Honeybee, and Bombyx, focusing on thermal comfort, energy demand, and global warming potential (GWP). - **Sensitivity Analysis and Design Optimization**: Identification of key design parameters (e.g. WWR, material layering, orientation) and their impact on performance, leading to informed suggestions for design refinement. - **Knowledge Synthesis**: Structuring findings into heuristic guidelines for architects and engineers working in comparable contexts, with consideration for site-specific climate and material availability.

This research is expected to deliver: - Performance Simulation Models of the Humanitas Case Study building, including baseline and improved scenarios. - Design Guidelines and Rules of Thumb for integrating earthen and timber elements in buildings located in temperate and mountainous climates, potentially transferable to other regions. - Technical Documentation or Code Snippets for simulation workflows (e.g., Grasshopper scripts, WUFI configurations), to support future projects. The student and supervisors will work together to create a detailed description of the tasks and goals, which will be based on the background and interests of the selected candidate.

Illias Hischier (illias.hischier@arch.ethz.ch)

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This project is set to limited visibility by its publisher. To see the project description you need to log in at SiROP. Please follow these instructions: - Click link "Open this project..." below. - Log in to SiROP using your university login or create an account to see the details. If your affiliation is not created automatically, please follow these instructions: http://bit.ly/sirop-affiliate

The primary objective of this project is to develop a machine learning-based approach to replace the repeated optimization procedures currently used for real-time flexibility quantification in buildings. Specifically, the research focuses on predicting flexibility envelopes, which quantify a building’s energy flexibility and are represented as a three-dimensional surface spanned by lead time, feasible power levels, and the corresponding maximum sustained duration (see Figure for an example). In our previous work, each point on this 3D surface was predicted individually—i.e., the maximum sustained duration was estimated for specific combinations of lead time and power level. While effective, this point-wise prediction approach is limited in efficiency and scalability. The aim of this project is to predict the entire 3D grid (or image-like structure) at once using advanced machine learning models, such as Transformers or Convolutional Neural Networks (CNNs). These models are well-suited for structured data and offer the potential for both high accuracy and fast inference.

**Literature Review:** Conduct a comprehensive review and comparison of machine learning techniques suitable for image or 3D grid prediction tasks. **Model Development:** Design and implement a machine learning algorithm capable of predicting the full flexibility envelope surface from historical data. **Evaluation:** Assess the performance of the proposed method using real-world data from the NEST building in Dübendorf, Switzerland, through simulation-based experiments.

mina.montazeri@empa.ch carl.remlinger@epfl.ch

Volume control is a long-standing research problem aimed at improving the precision and robustness of automated pipetting systems. Traditional pipetting methods rely on controlling liquid volumes indirectly through plunger displacement and predefined lookup tables specific to individual liquids. In contrast, this project explores an innovative approach where the volume of liquid in the pipette tip is controlled directly by considering internal air pressure according to the ideal gas law. However, there are several challenges that need to be addressed before deploying the controller in the real-world system including technical challenges, such as stick-slip friction, Helmholtz pressure oscillations, and other physical phenomena such as highly varying viscosities, which have thus far limited the robustness of the system. Additionally, factors such as liquid evaporation—causing unpredictable changes in the air dead-volume—and liquid retention effects, characterized by thin-film formation that leaves residual liquid in pipette tips after dispensing, further complicate precise volume control. The project is offered in collaboration with our industry partner Hamilton Robotics. It is a division of Hamilton Company, specializes in developing and manufacturing precision measurement devices and automated liquid handling workstations for the scientific community. Their product portfolio includes platforms like Microlab® VANTAGE, Microlab® STAR, Microlab® NIMBUS, and Microlab® Prep, known for their reliability, performance, and flexibility. Founded in 1950 by Clark Hamilton, the inventor of the microliter syringe, the company has grown into a global leader in laboratory automation, employing approximately 3,000 individuals worldwide.

Mathematical Modeling: Developing a comprehensive mathematical representation of the precision pipette hardware (MagPip). Data for system identification will be acquired through pressure sensors for accurate internal air pressure measurement. Control Architecture Design: Designing a robust control system that utilizes the mathematical model for the precision volume control subject to nonlinearities and disturbances. Several methods will be explored and compared. Simulation and Real-world Experimentation: Implementing the controller on a real-world prototype.

Muhammad Zakwan (mzakwan@ethz.ch) Efe C. Balta (efe.balta@inspire.ch)

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The primary objective of this research is to develop a mechanism that effectively distributes flexibility requests generated by a central system among its associated buildings. This allocation mechanism should ensure that all buildings contribute to flexibility provision while optimizing both social and economic welfare. It achieves this by determining the specific adjustments in energy consumption and the corresponding price for each building.

The project will involve the following tasks: 1.Literature Review: Explore different types of flexibility provisions from the grid and how buildings can effectively fulfill these requirements. \item Problem Formulation: Model the flexibility allocation problem as an optimization problem that considers operational constraints and social and economic welfare maximization. 2.Methodology Development: Investigate potential methods for solving the allocation problem, including game-theoretic frameworks and multi-agent optimization techniques, and develop an optimization-based allocation mechanism that distributes flexibility provisions among buildings to maximize social welfare while ensuring compliance with grid requirements. 3.Evaluation and Validation: Validate the proposed mechanism using real-world data from the NEST building in Dübendorf, Switzerland, assessing its effectiveness in meeting flexibility goals.

benita.nortmann@empa.ch mina.montazeri@empa.ch federica.bellizio@empa.ch

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