Open Master's Thesis Positions

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Nonlinear, data-driven control methods have gained popularity, yet (often) lack stability guarantees. Lyapunov/energy function methods provide a promising tool for addressing this challenge. One method for designing a stable controller is to use standard, LTI System Identification to learn the state space model, and then a neural network for the Lyapunov function and the controller [1]. This project will take a similar approach, but remove the intermediate System Identification step. That is, the project proposes to learn the neural network-defined energy function and controller directly from data, such that the controlled system is stable and optimal. Specifically, the “QSR” neural network architecture will be used, generalizing [2], as it allows for energy function-based stability guarantees. This will build off a recent submitted work which used the QSR function and a system model. The application will be frequency control of power systems. Specifically, the IEEE 37 Bus Feeder, for which a model and code are available from a recent publication in Neurips [3]. [1] Zhou, Ruikun, et al. "Neural lyapunov control of unknown nonlinear systems with stability guarantees." Advances in Neural Information Processing Systems 35 (2022): 29113-29125. [2] Koch, Anne, Julian Berberich, and Frank Allgöwer. "Provably robust verification of dissipativity properties from data." IEEE Transactions on Automatic Control 67.8 (2021): 4248-4255. [3] Cui, Wenqi, et al. "Structured neural-pi control with end-to-end stability and output tracking guarantees." Advances in Neural Information Processing Systems 36 (2024).

The student will: 1. Study dissipativity theory and data-driven control. 2. Review relevant literature on neural network control design and dissipativity. 3. Conduct simulation on power systems. 4. Final report and presentation. If the final results are adequate, they can potentially be written in a paper.

Please send your CV and transcript of records via email to Dr. Han Wang {hanwang@control.ee.ethz.ch} and Dr. Keith Moffat {kmoffat@ethz.ch}.

Model predictive control (MPC) is a powerful model-based control technique that received significant attention both in industry and academia thanks to its ability to efficiently obtain control inputs that are explicitly optimized for a predefined objective and fulfill process constraints. Designing the cost function and the constraints of an MPC controller is a question that has been thoroughly researched in the literature. Recently, several algorithms have been proposed to automatically obtain MPC controllers that are tailored for a specific task. Despite the great performance benefits that this automated tuning can bring, the MPC solutions one obtains do not work well if the task changes over time. In this case, to retain satisfactory performance, the controller needs to be re-tuned, resulting in significant computational effort. In this project, we wish to alleviate the computational effort required for re-tuning 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 the implementation of meta-learning to design an MPC controller for an experimental motion system. To achieve this, the study will first focus on constructing a gradient-based algorithm to automatically design the parameters of an MPC controller for a given task. Subsequently, a meta-learning algorithm will be applied to dynamically fine-tune these parameters in response to environmental changes, such as tracking references with different shapes. The student will implement the devised algorithms on the physical ball-on-a-plate system (see Figure 1), a mechanically actuated plate upon which a ball is balanced. The experimental system can be effectively represented as as LTI system with a nonlinear friction. It is located in the Automatic Control Laboratory, and serves as an ideal test-bed for evaluating learning-based control strategies. In the process, the student will be faced with a variety of topics of arguable importance in modern engineering, including control and machine learning. The goals of the project are as follows: - familiarize with the existing code base and with the interface of the ball-on-a-plate system; - construct and deploy a tuning algorithm to improve the performance of the MPC controller; - apply meta-learning algorithms to adaptively tune the control parameters to accommodate environmental variations or changes in the control task. _Publications_: Some of the results from the project are expected to be utilized in research publications from the lab. If the final results are promising they can be turned into a standalone publication as well.

We are looking for motivated students with some prior knowledge about 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 nikschmid@ethz.ch and rzuliani@ethz.ch.

The widespread adoption of electric vehicles (EVs) is transforming the energy and transportation sectors, presenting both challenges and opportunities for power grid management. As EV penetration grows, the charging behavior of EVs introduces significant variability to electricity demand, often leading to demand peaks that strain existing infrastructure. However, this challenge also presents a unique opportunity to leverage the flexibility of EV charging schedules. By strategically adjusting charging times and rates, EVs can contribute to demand-side management, enhance grid stability, and support the integration of renewable energy sources. In addition, taking into account people’s transportation behavior on a spatial dimension may lead to further improvements and provide a basis for optimal charging station placement.

This thesis aims to develop a comprehensive framework for spatio-temporal demand flexibility estimation for EV charging by leveraging data-driven modeling, time series forecasting and mathematical optimization. The goal is to find actionable strategies for optimizing demand flexibility on the basis of real-world data from EV charging infrastructure and mobility patterns. The general tasks are as follows: - Conduct a comprehensive literature review about spatio-temporal forecasting and demand flexibility estimation in the EV context - Analyze spatial and temporal EV charging patterns using the provided real-world data set - Propose a spatio-temporal estimation method for a Swiss case study and formulate a constrained optimization problem to suggest actionable strategies

leandro.vonkrannichfeldt@empa.ch

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**Research Questions** 1. How can existing thermal infrastructure be integrated into district thermal network designs to optimize energy performance and scalability? 2. What phasing strategies can maximize ROI for thermal network implementation under investment constraints? 3. How can thermal network performance feedback inform urban design metrics, such as energy efficiency, carbon footprint, and phasing strategies? **Methodology** 1. Literature Review Survey global best practices for integrating existing thermal infrastructure into new network designs. Explore how thermal networks interact with and influence urban design metrics. 2. Network Model Development Incorporate existing infrastructure into thermal network design (pipes, plants, pumps). Develop phasing strategies for incremental implementation. Integrate metrics to evaluate the impact of network designs on urban parameters: Energy efficiency (e.g., network losses, plant capacity factor). Environmental impact (e.g., emissions). Land use efficiency and spatial constraints. 3. Feedback Loop for Urban Design Use the enhanced network model to evaluate urban design scenarios. Quantify how urban form changes (e.g., building volume, land use type mix, phasing and retrofitting strategy) affect network performance (e.g., return of investment) and vice versa. 4. Case studies Zurich: Heating-dominant network design. Singapore: Cooling-dominant network design. Shanghai: Mixed heating and cooling requirements. **Candidate Description** We are looking for a highly motivated candidate with a background in architecture/urban design (with a strong interest in engineering/computational modelling/data science), or engineering (with a strong interest in architecture/urban design). You should have good verbal and written communication skills in English. Experience in CAD (e.g., Rhino 3D), parametric design programs (e.g., Grasshopper), basic programming using Python, urban building energy modelling (UBEM) and an understanding of district heating/cooling engineering are essential. You should be impact-driven with a product-thinking mindset. The research outcome is expected to be integrated into the open-source City Energy Analyst benefiting the global UBEM community for decarbonising cities through urban and architectural design.

**Deliverables** 1. Technical Report: Framework for integrating existing infrastructure and phasing district thermal networks. Metrics for evaluating the interactions between urban design and network performance. Results from case studies in Zurich, Singapore, and Shanghai. Heuristics on urban design and district thermal network engineering interplays. 2. Computational Model: A robust model for thermal network design and phasing strategies. If feasible, a prototype for integration into tools like CEA, enhancing its thermal network feature.

The master thesis will be supervised by Prof. Dr. Arno Schlüter, Dr. Zhongming Shi and Mathias Niffeler. Please send your statement of interest including a short CV and current transcript of records to Dr. Zhongming Shi(shi@arch.ethz.ch). Please direct questions regarding the position to the same e-mail. Information on the A/S Research Group can be found here: https://systems.arch.ethz.ch/ Information on the City Energy Analyst can be found here: https://www.cityenergyanalyst.com/

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**Objectives** The research aims to: Plan and execute mechanical tests (bending, compression, tensile) to compare the structural performance of various polymers for 3D printing. Compare the mechanical properties of these materials against the performance requirements for lightweight, self-supporting facades. Use testing data, if time allows, to inform computational assessments of free-form facades under realistic load conditions. **Methodology and Tools** Experiment Planning and Specimen Preparation: - Design an experimental plan, including mechanical testing protocols. - Coordinate the fabrication and preparation of test specimens Mechanical Testing: - Perform bending, impact, and tensile strength tests using relevant machines - Analyze stress-strain behavior, failure modes, and performance metrics Computational Assessment (Optional): - Use testing data to develop computational models and simulate facade performance under realistic loading conditions. Tools: - Universal Testing Machines for mechanical tests (ETH DBAUG). - Standardized testing protocols (e.g., ASTM or ISO standards). - FEA software (e.g., Abaqus, ANSYS) if simulation tasks are conducted. **Expected Outcomes** - A detailed experimental plan and prepared specimens for testing. - Datasets on bending, impact, and tensile strength of various 3D printing polymers. - Comparative analysis of material performance relative to facade requirements. - Initial computational models assessing the structural performance of 3DP façade design based on mechanical testing results (optional) **We are looking for** Students with a background and interest in material science, structural engineering, and experimental research. Preferred skills include: - Experience in planning and conducting material testing experiments. - Knowledge of mechanical testing protocols and data analysis. **References:** Leschok*, Matthias, Ina Cheibas*, Valeria Piccioni* et al., “3D Printing Facades: Design, Fabrication, and Assessment Methods.” Automation in Construction 152 (August 2023): 104918. https://doi.org/10.1016/j.autcon.2023.104918. Sepahi, Mohammad Taregh et al., “Mechanical Properties of 3D-Printed Parts Made of Polyethylene Terephthalate Glycol.” Journal of Materials Engineering and Performance 30, no. 9 (September 2021): 6851–61. https://doi.org/10.1007/s11665-021-06032-4. Anderson, Isabelle. “Mechanical Properties of Specimens 3D Printed with Virgin and Recycled Polylactic Acid.” 3D Printing and Additive Manufacturing 4, no. 2 (June 2017): 110–15. https://doi.org/10.1089/3dp.2016.0054

Contacts and supervision team: Valeria Piccioni (piccioni@arch.ethz.ch), Francesco Milano (milano@arch.ethz.ch), Vlad-Alexandru Silvestru (silvestru@ibk.baug.ethz.ch)

**Objectives** The project's main goal is to develop a predictive model for energy consumption and emissions in robotic 3D printing to support data-driven process analysis. Additional Objectives: - Analyze company-provided data to identify key drivers of energy consumption. - Build a machine learning-based model to predict energy usage under various printing conditions. - Integrate emissions estimation into the model using energy source emission factors. - Provide insights into the environmental performance of robotic 3D printing processes. **Methodology and Tools** - Data Analysis: Utilize fabrication data provided by Saeki Robotics. - Predictive Modeling: Use machine learning techniques to develop forecasting models. - Validation and Process Insights: Validate the data-driven model with physics-based/analytical models and extract key findings on process energy and emissions profiles. **Expected Outcomes** - A validated predictive model for energy consumption and emissions. - Insights into the main contributors to energy usage and emissions in robotic 3D printing. - A comprehensive analysis of the environmental performance of printing processes. **We are looking for** A motivated student with a background in engineering, data science, or sustainability. Experience with programming, machine learning, and 3D printing technologies is a plus. **References:** Yan, Z., et al. (2023): Hybrid mechanism-based and data-driven approach to forecast energy consumption of FDM. Journal of Cleaner Production. DOI: 10.1016/j.jclepro.2023.137500 Ghungrad, S., et al. (2024): Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing. Journal of Manufacturing Processes. DOI: 10.1016/j.mfglet.2024.09.017 Yan, Z., et al. (2022): A new method of predicting the energy consumption of additive manufacturing considering the component working state. Sustainability. DOI: 10.3390/su14073757

For more information or to apply, contact piccioni@arch.ethz.ch.

In this thesis, up to three façade wall assemblies will be designed in collaboration with A/S researchers and tested under the controlled conditions of the Zero Carbon Building Systems Lab’s Solar Simulator. A sensor array—including, for instance, 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, or MATLAB) and compared with existing literature.

The primary goal of this thesis is to evaluate the performance of up to three different wall constructions in terms of their contribution to outdoor 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.

Modern control methods often rely on explicit online computation. That is, for every time step, the controller needs to perform some optimization, numerical algebra or integration in order to determine the inputs. Naturally, such methods give rise a trade-off: For the same implementation more accuracy requires more computation time. Given the extremely high actuation frequency of modern control plants, and the increasing scale of, for instance, online optimization problems, the analysis and acceleration of such implementations is of great interest. Generally, performance analysis of such controllers relies on time scale separation. In simple terms, we assume that ‘enough’ computation can be performed to obtain a control signal that is ‘close’ to optimal. However, this leaves a number of important question unanswered: ˆ Beyond the analysis of closed loop stability, can we derive or minimize bounds on the transient behavior? ˆ Can we characterize precisely which algorithms in a specific class yield closed-loop stability? ˆ Given the previous, can we design algorithms that do not just converge quickly, but yield robustly stable closed loops? In order to understand these question, and more generally the behavior of closed loops between numerical methods and dynamical systems, this project approaches the algorithm as a dynamical system itself. In doing so, the usual language of convergence of algorithms can be viewed as a special case of stability theory. Moreover, beyond the stability analysis, this opens up the use of controltheoretical methods to analyze for instance noise rejection and, more generally, closed-loops between plants and algorithms.

The student will: 1. learn and understand concepts related to the topic, including incremental dissipativity and numerical analysis; 2. explore and review the relevant literature; 3. perform simulations on closed loop systems to validate and compare different methods; 4. bring together the theory and analyze the results;

Dr. Jaap Eising, jeising@ethz.ch.

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Please look into the attached document.

1. Study of opinion dynamics 2. Literature review on online feedback optimization and implementation on MATLAB/Python/Julia; 3. Empirical and theoretical analysis of OFO algorithms to identify influential users in a social network 4. Final report and presentation.

We are looking for a talented and highly motivated student with a background in Automatic Control/Applied Mathematics or related fields, optimization and MATLAB/Python/Julia programming. No specific experience in optimization algorithms and/ or social networks is necessary. The student must be enrolled in a master program. If you have any questions regarding the project, please do not hesitate to get in touch. If you would like to apply, please send a brief email with your CV (including lists of projects and optionally any document helpful to evaluate your background) and current grade transcript in PDF to: schandraseka@ethz.ch.

Online feedback optimization (OFO) is a controller design paradigm for optimizing the steady- state of a dynamical system. OFO uses an optimization algorithm as a feedback controller, and exploits real-time measurements to cope with the uncertainty on the system dynamics and disturbances. On the downside, existing stability guarantees for OFO require the controller to be much slower than the dynamical system; namely, the control gain has to be very small. This “timescale separation” possibly affects transient performance, settling time, and responsiveness to disturbances —especially in problems with high temporal variability. This project investigates conditions under which stability of the closed-loop can be ensured without requiring any timescale separation. To this end, we use the theory of “monotone system”, which applies for instance to freeway traffic models. The student will have the opportunity to learn about OFO and monotone systems, before applying these concepts to a ramp metering problem, both via analytical analysis and numerical evaluation.

1. Literature review on topics in online feedback optimization and monotone systems 2. Become familiar with OFO algorithms and their implementation (MATLAB/Python/Julia) 3. Analysis and numerical evaluation of OFO algorithms for congestion control in a traffic network model 4. Final report and presentation.

We are looking for a talented and highly motivated student with a background in Automatic Control/Applied Mathematics or related fields, optimization and MATLAB/Python/Julia programming. The student must be enrolled in a master program. If you have further questions on this project, please do not hesitate to get in contact with us. If you would like to apply for this project, please email your CV (including lists of projects and optionally any document helpful to evaluate your background) and current grade transcript in PDF to: schandraseka@ethz.ch, mbianch@ethz.ch.

The integration of renewable energy sources and distributed generation in modern power systems has increased significantly. This resulted in an increased use of power-electronics-based converters, changing the dynamical behaviour of the grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. It is also used for optimizing the closed-loop performance via, e.g., adaptive control design. However, due to the grid's dynamic and nonlinear nature, impedance estimation becomes particularly challenging, in particular when conducted in real-time under closed-loop operation (see e.g., [2]). A new non-parametric identification method has shown potential for accurate and rapid grid impedance identification. While initial simulations have demonstrated the potential of the method, experimental validation using hardware is crucial to evaluate the method's performance under real-world conditions. This project aims to use two converter emulators ([3]) available in the Automatic Control Laboratory to experimentally validate the new approach. The main goals are to replicate realistic converter/grid conditions, assess the accuracy and robustness of the method, and to explore the limitations and performance boundaries of the method. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] https://imperix.com/products/power-electronics-test-bench/ **Prerequisites** We are looking for a motivated student with a background in power systems/power electronics, and a previous exposure to system identification or signal processing. Knowledge of grid-connected converters and impedance estimation methods would be highly beneficial. Familiarity with experimental configurations and lab instrumentation is desirable. Competitive candidates will have prior research or work experience that demonstrate the ability to learn and problem-solve independently.

1. _Literature Review_: Review state-of-the-art literature on non-parameteric and parameteric grid impedance identification techniques, and learn the theoretical basis of the LPM method. 2. _Set up an experimental configuration_: Use the available imperix power electronics test benches to replicate a grid-connected converter scenario, allowing for safe and accurate data collection for impedance estimation. 3. _Data collection and analysis_: Collect experimental data under a variety of grid and excitation conditions. Analyze the accuracy and robustness of the LPM method, and compare to alternative estimation methods. 4. _Results interpretation and conclusions_: Document the findings, noting any discrepancies between simulation-based results and experimental outcomes. Propose potential improvements to the estimation method or additional experiments for further study, with the aim of ensuring reliability and robustness in real-world applications. 5. _Benchmark comparisons_: Examine the performance of alternative methods in a benchmark comparison study, highlighting the differences in terms of accuracy, used devices and experimentation time. 6. _Reporting_: Prepare a final report and a 30 minutes final presentation.

Please send your CV and transcript of records via email to Mohamed Abdalmoaty mabdalmoaty@ethz.ch and Xiuqiang He xiuqhe@ethz.ch.

The use of renewable energy sources and distributed generation, such as wind turbines and solar power plants, in modern power systems has been increasing significantly. This resulted in a heightened use of power-electronics-based converters, changing the dynamical behaviour of the power grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. However, due to the grid's dynamic and nonlinear nature, impedance estimation is particularly challenging (see e.g., [2]). The grid-connected converter's controller must maintain stable operation while simultaneously allowing the injection of small excitation signals necessary for grid impedance estimation. This creates a fundamental trade-off: while a good controller and converter-grid interface filter improve system stability and disturbance rejection, they can also suppress the grid's response to the excitation, reducing the estimation accuracy. On the other hand, injecting larger excitation signals could improve the quality of estimation but risks triggering the converter protection system and/or exciting nonlinear grid behaviors. Moreover, it is not clear what the best class of excitation signals is nor the best injection point within the converter controller. This project aims to address this trade-off and questions by developing optimal excitation schemes for grid impedance estimation using grid-connected converters. A main goal is to obtain a good signal-to-noise ratio at the frequencies of interest while respecting experimental time and signal magnitude constraints. A class of excitation signals of potential interest is random phase multisines [3]. Recent related results in the literature are [4,5] which are based on modelling the noise and the use of multiple sinusoidal injections. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] Godfrey, Keith, ed. Perturbation signals for system identification. Prentice Hall International, 1993. [4] Y. Zhu, et.al, "Injection Amplitude Guidance for Impedance Measurement in Power Systems," in IEEE Transactions on Power Electronics, vol. 38, no. 6, June 2023 [5] H. Li, et.al., "A Noise Interference Suppression Method With Optimized Frequency Adjustment for Impedance Measurement," in IEEE Transactions on Industrial Electronics, vol. 71, no. 12, Dec. 2024 **Prerequisits** We are looking for a motivated student with excellent written and verbal communication skills, and the ability to learn and problem-solve independently. Competitive candidate will have a background in systems & control, signal processing, and power systems/electronics. A prior experience or coursework in system identification and/or grid-connected converters would be highly beneficial. Proficiency in MATLAB/Simulink or a similar simulation environment is required for implementing and testing the methods.

1. _Literature review & problem definition_: Conduct a literature review on existing grid-impedance estimation techniques, as well as excitation design schemes. Define the requirements and limitations for accurately estimating grid impedance in a real-time, closed-loop setting, while addressing factors that impact estimation accuracy under practical conditions (e.g., experiment time, amplitude of excitation signals, etc). 2. _Development of optimal excitation framework_: Formulate an "optimal" excitation framework tailored for grid impedance estimation via grid-connected converters. 3. _Excitation signal design_: Generate and test the excitation signal in simulation, highlighting the impact on estimation accuracy. Explore the potential of iteratively refining the signal design to improve accuracy. 4. _Benchmark comparisons in simulation_: Compare the performance of your method(s) to existing ones as well as those using arbitrary excitation on benchmark models. Analyze the results to determine the practical effectiveness of the proposed approach for real-time applications in power systems. 5. _Reporting_: Prepare a final report and a 30 minutes final presentation.

Please send your CV and transcript of records via email to Mohamed Abdalmoaty mabdalmoaty@ethz.ch and Xiuqiang He xiuqhe@ethz.ch.

Please, see attached file.

Through this project, the student will gain insights into advanced game-theoretic concepts, focusing on the issues of Stackelberg games, statistical learning and learning in games. The student will develop skills in working with mathematical models of games and optimization problems, data analysis, as well as translating theoretical findings in a well-written report/paper. For more detailed description, please, read attached file.

Dr. Ilia Shilov: ishilov@ethz.ch

Designing suitable controllers for building energy systems presents considerable challenges due to the variability in system characteristics from one building to another and the seasonal variation in disturbances. Additionally, the inherent nonlinearities of HVAC system dynamics complicate controller design. Current HVAC controller tuning processes are highly time-consuming, and controllers often need recalibration following building retrofits. Developing adaptive controllers that can autonomously adjust to specific HVAC systems and respond to configurational and seasonal changes is thus of high interest. In pursuit of these objectives, this project will: - explore control strategies from the domain of data-driven control like Data-enabled Predictive Control (DeePC) and Reinforcement Learning (RL). - examine potential architectures, such as Hierarchical RL (HRL), to optimally implement data-driven controllers for HVAC applications. - assess the performance of the proposed approach in a representative building control scenario. The student can utilize several existing building and component models. Familiarization with these resources and integration of the developed code in the existing framework is expected. The project will primarily involve simulation work, with no experimental activities planned. Case studies based on real-world data may also be explored.

The primary goals of the project include: - Investigate and evaluate control methods and architectures from the field of data-driven control. - Implement and assess these methods for building energy system control within a simulation environment. The findings from this project will potentially contribute to research publications from the laboratory. If results are particularly promising, they may serve as the foundation for a stand-alone publication.

We seek a highly motivated student with a background in control. Proficiency in numerical methods, optimization, thermal system modeling, building control, and familiarity with data-driven techniques or machine learning are advantageous. - Specific experience with building systems is not required. - Strong foundation in control theory, ideally also for nonlinear control systems. - Strong interest in applying and adapting/developing data-driven control methods. - Proficiency in Python and/or Matlab programming languages. - Knowledge of modeling and simulating dynamic systems using Matlab/Simulink is a plus. - Excellent proficiency in English and strong communication skills are essential. - Depending on the project’s focus, the student will collaborate with Belimo Automation’s research group and may have limited access to necessary company resources for development and testing. Please send your resume/CV (including relevant publications/projects) and transcript of records in PDF format to efe.balta@inspire.ch and bspoek@ethz.ch.

Please, see attached file.

The student will gain insights into advanced game-theoretic concepts, focusing on the issues of Stackelberg games, imperfect information, credible threats, and bounded rationality. The student will develop skills in developing mathematical models of games and optimization problems, simulating games, as well as translate theoretical findings into well-written report/paper. For more detailed description, please, see the file attached.

Dr. Ilia Shilov: ishilov@ethz.ch

The key steps envisioned to complete the project are as follows: 1. Literature review - conduct a literature review on the modeling of BTES systems and district heating and cooling networks, with particular emphasis on the modelica language (4 weeks). 2. Model development - Develop a detailed Modelica-based model of the high-temperature BTES system of the Empa campus, including interactions with the district heating and cooling networks (6 weeks). 3. System verification - Use the developed model to verify the current system design and identify areas for improvement (6 weeks). 4. Design improvement suggestions - Analyze the results and propose potential design modifications (4 weeks). 5. Documentation and report writing - Create detailed documentation of the model and writing of the final report (4 weeks).

This project aims to develop high-fidelity models of the high-temperature borehole thermal energy storage system, focusing on its integration with the district heating networks of the Empa campus. The model will be implemented using the Modelica language within the open-access OpenModelica software. The developed model will be documented in detail to ensure future usability and maintainability. The model will also contribute to the ongoing digital twin efforts of the Empa campus, allowing for real-time verification and optimization of system performance. The following key objectives will be addressed: • Verification of system design: Assess the current design of the HT-BTES system and identify any discrepancies between the design assumptions and real-world operation; • Suggest design and operational improvements: Propose modifications or enhancements to the current system design and operational strategies to improve its long-term sustainability and integration within the district network.

gabriele.humbert@empa.ch

The envisioned steps for the project are: 1. Literature review: Explore relevant literature on hydrogen technologies, waste heat recovery, and their application in small-scale districts and energy communities. 2. Scenarios definition: Based on-going projects at Empa, specific case studies and scenarios will be defined. 3. Model Development: Extend existing models, such as those in the EhubX optimization tool [4], to incorporate and calibrate hydrogen storage, fuel cells, electrolyzes and waste heat recovery. 4. Numerical Simulations: Conduct sensitivity analyses to evaluate the performance of different configurations and scenarios. 5. Analysis and Reporting: Analyze results and provide recommendations on how to integrate hydrogen technologies and waste heat recovery to achieve decarbonization targets

The primary goal of this thesis is to develop and test models for the integration of hydrogen technologies and waste heat recovery in prosumer energy communities. The research will address the following key questions: • What is the optimal configuration for integrating hydrogen technologies in energy communities? • In which scenarios do hydrogen technologies provide the most significant benefits in terms of cost reduction and carbon savings?

gabriele.humbert@empa.ch