Open Master's Thesis Positions

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 project investigates the development of a co-axial extrusion methods for large-scale 3D printing bio-cementation structures. The extruded paste will host microorganisms such as S.Pasteurii, capable of precipitating calcite (MICP) to create bio-concrete structures. A robotic paste 3D printing platform will be used for the fabrication process; the bio-paste will be precipitated and calcified by the bacterial activity reinforcing the material.

Goal This project aims to optimize the robotic 3D printing systems by developing a co-axial extrusion process. This project will include the following tasks: Developing, prototyping and testing of the co-axial extruder. Testing the materials in the robotic fabrication set-up. The experimental work will be carried out in the new bio lab of the ALIVE initiative at HCI - Hönggerberg ETH. A background in mechanical engineering, architecture, robotics, is highly desirable but optional. The student should be motivated, self-driven, and keen to work in a dynamic, interdisciplinary research environment.

Karen Antorveza antorvezapaez@arch.ethz.ch

The need to ensure the efficient operation of dynamical systems of increasing complexity (e.g., smart buildings and interconnected power grids) has recently sparked renewed interest in “direct data-driven control” methods. This term refers to designing controllers using exclusively measured data, without the identification of a model ― the most expensive and time-consuming process. Since the true plant is assumed to be unknown, the goal consists in finding a controller that can work for all the plausible systems ― i.e., all those compatible with the available measurements. One way to recast and solve this challenging robust-control problem is by using SOS optimization; this solution has been explored in the recent literature, but with the drawback of heavily relying on state measurement (or reconstruction).

The goal of this project is to develop analysis and control techniques for an unknown linear system, only based on a few noisy measurements. To this end, we use tools from polynomial optimization and SOS programming: this approach allows us to deal with the challenging case of output (rather than state) measurement, and to incorporate any prior knowledge on the system (e.g., sparsity) in the controller design. The student will have the opportunity to learn about SOS polynomials and data-driven control, before analytically investigating solution techniques and evaluating their performance numerically.

michael.schneeberger@fhnw.ch, mbianch@ethz.ch

As modern technology digitizes sectors including agriculture, power grids, and transportation networks, a network of interconnected and self-interested decision-makers emerges as a prominent feature of urban supply chains. Whereas traditional supply chains tend to follow a multi-tier, tree-like structure, e.g. producers supply distributors, who supply retailers, who supply consumers. Increased global mobility and the formation of retail giants have introduced greater complexity in supply chain pathways. For example, a local bakery may supply a grocery store chain with baked goods, while the grocery store chain may supply the bakery with flour and other baking necessities. How do these complex networks affect individual decision-making, and are there local strategies that will both increase individual player profits as well as stabilize the global supply throughput? We will extend existing models on the two-player supply chain game to multi-player supply chains with non-trivial connectivity structures modeled via graph theory, and investigate various player dynamics (e.g. consensus, best response, gradient descent) in combination with different interconnection structures(e.g. trees, small-world network, star) to study the stability of the overall supply chain.

This project has the following goals. 1. Learn about supply chain modeling from operation research, game theory under shared constraints, and different graph-theoretical metrics for characterizing connectivity. 2. Formulate a multi-player supply chain game and derive conditions for the existence of a Nash equilibrium. 3. Characterize the stability of gradient descent, consensus and/or best response dynamics against disturbances and investigate their convergence properties. 4. Verify convergence guarantees on a real data set for agriculture supply chains and electricity markets.

Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to {huilih,degiulia, shall\}@ethz.ch.

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

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

Buildings are major contributors to global energy consumption and carbon emissions. The rise of information and communication technology (ICT) infrastructure has made digital transformation in the building sector increasingly feasible. However, the cost-effectiveness of sensor-based systems for energy management remains an open question. The trade-off between investing in sensor-based systems versus the resulting cost savings during building operation will be investigated in the project. **Project description** The project will be based on Empa’s famous NEST building and its digital twin, and co-supervised between IfA and Empa. The key steps envisioned for the completion of the project are as follows: 1. Conduct a literature review on energy management systems in buildings (1 month). 2. Set up a baseline scenario with a minimal set of sensors and a basic control system (1 month). 3. Implement alternative scenarios with different levels of sensing and actuating capabilities and (optional) control strategies (2 month). 4. Compare with the baseline scenario and establish a Pareto front to analyze trade- offs between sensor/actor investment and control performance (1 month). 5. Summarize the results and write the final report (1 month). **Prerequisites** A good understanding of numerical modeling and familiarity with Python are required for the project. Please submit your current CV and academic transcripts with your application

The goal of this project is to find the optimal trade-off between investing in an improved control solution for a building by augmenting its sensing and actuating capabilities, and the resulting reduction of operating cost of the building.

IfA: Claudia Fischer (c.fischer[at]control.ee.ethz.ch) Empa: Hanmin Cai (hanmin.cai[at]empa.ch)

The project will mainly focus on the preparation of an experimental campaign oriented to understand the behavior and properties of an hybrid textile/mycelium composite. Particular emphasis will be given to the bio composite design and its mechanical and thermal assessment across different incubation times ( 30,60,90 days).

The goals of the experimental campaign is to fabricate different types of bio-composites and to evaluate their performance. The project will include the following tasks: a)Learning the basic protocols for producing a mycelium based composites b)Design of the experimental setup c)Perform mechanical and thermal tests after different cycles of incubation time The experimental work will be carried out in the BFL- Bio-fabrication Lab in HIB-Hönggberger ETH. A Background in material science, Civil or Mechanical Engineering is highly recommended by not necessary. Students should be motivated, proactive and available to work in a team environment.

Tiziano Derme: derme@arch.ethz.ch

Dr. Giulia De Pasquale, degiulia@ethz.ch Dr. Ambra Amico, aamico@ethz.ch Dr. Giacomo Vaccario, giacomov@ethz.ch Dr. Raffaele Soloperto, soloperr@ethz.ch

Feedback optimization is a modern control method, where optimization algorithms are directly interconnected in closed-loop with a physical plant. The advantage is that the system is driven to an efficient point defined by an optimization problem, rather than to an already known reference; in this way, the controller can also quickly react to unpredictable changes in the environment. For instance, feedback optimization has been succesfully deployed in the control of power grids, where the ongoing energy transition is posing important new challenges, due to the volatility of renewable resources, distributed production and unknown power demand.

The goal of this project is to extend the known feedback optimization schemes, based on primal dynamics as gradient descent, to primal-dual dynamics (i.e., methods based on Lagrangian duality as ADMM). The advantage is that output constraints and nonsmoothness can be easily handled, in contrast to classical feedback optimization. We aim at studying analytically the stability of primal-dual feedback optimization schemes, and at evaluating their performance in the control of power systems, in terms of robustness, speed, capacity of dealing with nonconvexity and handling constraints. The student will be introduced to and taught on the concept of feedback optimization, and given a simple power system model and control problem that can be solved via primal-dual feedback optimization. After this initial phase, the student will search the literature for the most suitable models and problems, and will implement numerically the controllers to evaluate performance. The project can be adapted on the run if new interesting research directions arise. If the results are promising, they can be turned into a publication.

mbianch@ethz.ch, kmoffat@ethz.ch, bsaverio@ethz.ch

The aim of this project is threefold: 1. quantify the forecast error when predicting arrival/departure times and energy demands of the buses and impact on the V2X capabilities, 2. design an economic MPC-based controller for the optimal V2X operation of an energy hub together with its fleet of electric buses [1, 3], and 3. analyze the performance of the MPC-based controller in terms of energy cost reduction, technical/operational constraint violation and service revenue generation.

As a result of this project, the student will: - Learn about energy hub management and V2X capabilities of electric fleets - Develop model predictive control schemes in energy hub operation and fleet dispatch for demand-side flexibility provision - Validate synthesized controllers on simulations (or/and in real systems) - Engage in regular meetings with members from IfA and EMPA - Prepare a final presentation (∼20 minutes) and report (∼20 pages for SA, ∼50 pages for MA)

Federica Bellizio, federica.bellizio@empa.ch Marta Fochesato, mfochesato@ethz.ch Jared Miller, jarmiller@ethz.ch

The project will aim to create a comprehensive conceptual framework that can assess and quantify the resilience of smart energy systems. The framework will consider the interdependencies between system components and the ability of the system to maintain functionality during and after disturbances. The envisioned steps for the project are as follows: 1) Literature Review: Conduct a thorough review of existing literature on smart energy systems, with a focus on resilience assessment methodologies. 2) Framework Development: Develop a conceptual framework that integrates various components of smart energy systems and establishes indicators for resilience. 3) Data Collection: Gather and analyze data on residential energy systems, including user behavior patterns, system performance, and environmental impacts. 4) Case Study Implementation: Apply the conceptual framework to a case study involving a smart energy system as described in the background. 5) Validation: Validate the framework using simulated scenarios to assess the resilience of the system under different stress conditions. 6) Documentation: Document the entire process and findings in a comprehensive report.

Some of the key insights that can be derived from the data and the project: 1) How can resilience in smart energy systems be conceptualized and measured? 2) What are the critical components and interdependencies in a smart energy system that affect its resilience? 3) How do different stress scenarios impact the resilience of smart energy systems? 4) What strategies can be implemented to enhance the resilience of these systems?

Dr. Arnab Chatterjee - arnab.chatterjee@empa.ch

The proposed project will develop a common database of metering and sub-metering data from Swiss households. This data will be used to develop NILM algorithms and customer segmentation models. The project will also evaluate the demand flexibility potential of Swiss households and develop targeted demand flexibility programs. The project will develop new tools and knowledge that can be used to design and implement effective demand flexibility programs in Switzerland. The project will also contribute to the development of a more reliable and sustainable power system. The key steps envisioned for the completion of the project are as follows: 1. Literature review: A review of the literature on NILM, customer segmentation, and demand flexibility in the context of the Swiss energy sector will be conducted. This will provide a foundation for the development of the proposed framework and database. 2. Data collection and preparation: A common database of metering and sub-metering data from Swiss households will be prepared. The data will be collected from open-source datasets, collaboration partners, and Nest LL. The data will be cleaned, preprocessed, and standardized to ensure consistency and quality. 3. NILM algorithm development and validation: Popular NILM models found in the literature will be tested in the context of Swiss households to disaggregate energy consumption data into individual appliance loads. 4. Customer segmentation modeling: A customer segmentation model will be developed using the NILM-disaggregated data. The model will be used to identify different groups of customers based on their energy consumption patterns.

1. Development of black-box household model: Black-box models for household appliances will be developed using the NILM-disaggregated data and customer segmentation model. The models can be used to simulate and predict energy consumption under different conditions. 2. Demand flexibility analysis (Optional): The impact of demand flexibility on power system operation will be analyzed using the customer segmentation model, NILM-disaggregated data, and occupant comfort criteria.

Dr. Arnab Chatterjee - arnab.chatterjee@empa.ch Leandro Von Krannichfeldt - leandro.vonkrannichfeldt@empa.ch

See attached file for more detail

Software development of a scan path generator for a in-house built PBF machine

steffera@ethz.ch ebalta@ethz.ch

Buildings are responsible for about 42% of the final energy consumption and for 26% of the total CO2 emissions in Switzerland, with heating demand being the main driver with about 68% - finding applicable solutions to reduce the energy consumption in this area will have a big effect. The main constraints here are maintaining the comfort of tenants and users. Minimizing the energy consumption of the heating system and meanwhile respecting these constraints makes it a challenging problem. A widespread approach to optimizing heating demand is through adjustments of heating curve. We want to develop a heating curve parameter optimizing controlling system, short adaptive heating tuner, based on Bayesian Optimization, which is scalable, fully automated, and fast convergent to reduce the energy demand while maintaining the room comfort of the inhabitants. The adaptive heating tuner is formulated as an optimization problem, where the decision variables are the parameters that determine the heating curve. The heating curve is uniquely defined by the parameters A, B (see Figure 1, left). The decision variable is therefore denoted by x : x = (A, B). In addition to the decision variables we need to introduce a suitable cost function based on the energy consumption and comfort violations: f : X -> R where X denotes the set of feasible decision variables and R is a real number. Since the cost function of heating curve parameters from energy consumption and comfort violations is not given, the closed form of f is not given. Moreover, the value of f can only be determined by experiment. To evaluate f for a given x, we must first set the heating curve parameters to x, run the heating system for one day, measure the total energy consumption for heating and cooling and the room and outdoor temperatures. Then we can calculate the amount of comfort violations and find the value f(x). Therefore, we use Bayesian optimization techniques to solve the presented optimization problem, see Figure 2 for further details. In the first version of our algorithm, the parameter space of the heating curve is 4-dimensional, i.e. we can shift the coordinates of the points in the X or Y direction and restrict ourselves to a straight line between the points. This parameterization of the heating curve is referred to as a simple heating curve. In a further step, we are now interested in extending the heating curve to include non-linearities. The simplest extension is the so-called non-linear two-point curve, where a curved curve is introduced between the two points, after which we would add further points to the heating curve (see Figure 1, right). We are interested in the following aspects: to what extent do non-linearities affect our cost function, convergence and how much does this increase the computational effort? In the end, we are interested in finding an economically optimal curve in terms of computational effort and cost minimization. Validation is carried out using simulations based on design-builder models of the real test environments for NEST Unit UMAR and Sprint and the building provided by our industrial partner. Accomplishing the goals of this project requires numerical and experimental steps summarized in the lists given below (tentative and not finalized):

The main goal of your project will be the implementation of non-linearities and their validation. The first prototypes can be created on the same data as already used for the first version of the algorithm development. In a further step, new data can be added, especially regarding validation. The following sections outline the questions and deliverables to be addressed during the project. Completion of all tasks is not required for passing; rather, they serve to provide an overview of the project's scope and objectives: **Research Questions:** - How do non-linearities, such as the non-linear two-point curve, impact the optimization process of the heating curve parameters in terms of convergence and computational efficiency? - What is the comparative performance of non-linear heating curve models against traditional linear models in terms of energy savings and comfort maintenance? - How do different forms of non-linearities (e.g., sigmoidal curves, multi-point curves) influence the behavior of the heating system? - What are the optimal strategies for incorporating non-linearities into the adaptive heating tuner algorithm while ensuring fast convergence and scalability? - How do non-linear heating curve models perform under varying external conditions (e.g., different climate zones, building types) and what adjustments are necessary to accommodate these variations effectively? **Deliverables:** - A functioning algorithm for adaptive heating curve optimization, capable of incorporating non-linearities and demonstrating improved energy efficiency while maintaining user comfort. - Technical reports detailing the development process, algorithm design, and validation results. - Presentation materials for midterm presentation and final presentation - Open-source software code repository containing the developed algorithm, allowing for further collaboration, refinement, and adoption by the research community. - Pilot installation showcasing the practical application and benefits of the adaptive heating tuner in real building settings. **Project timeline:** _Phase 1: Project Initiation and Planning (Weeks 1-3)_ - Define project objectives, scope, and deliverables. - Conduct a literature review on heating curve optimization and Bayesian optimization techniques. - Familiarization with existing algorithms, focusing on linear heating curves. _Phase 2: Research and Algorithm Development (Weeks 4-8)_ - Develop non-linear extensions to the initial version of the adaptive heating curve optimization algorithm, starting with the non-linear two-point curves. - Conduct simulations to validate algorithm performance under various scenarios. - Document the algorithm development process and preliminary findings. _Phase 3: Midterm Presentation (Week 9)_ - Prepare a presentation summarizing project progress, including research findings, algorithm development, and initial validation results. - Present the midterm progress. - Receive feedback and suggestions for refining the algorithm and adjusting project priorities for the remainder of the timeline. _Phase 4: Non-Linear Algorithm Development (Weeks 10-14)_ - Extend the adaptive heating curve optimization algorithm to incorporate further non-linearities, such as multi-point curves. - Conduct additional simulations to assess the impact of non-linearities on algorithm performance, convergence, and computational effort. - Fine-tune algorithm parameters and optimization strategies based on simulation results. - Document the non-linear algorithm development process and compare performance metrics with linear counterparts. _Phase 5: Validation and Optimization (Weeks 15-19)_ - Validate the developed algorithm using real-world building data and simulations. - Optimize algorithm parameters to achieve the desired balance between energy efficiency and user comfort. - Conduct sensitivity analyses to understand the robustness of the algorithm under different operating conditions. - Prepare technical reports and documentation summarizing validation results and optimization strategies. _Phase 6: Finalization, Dissemination and project wrap up (Weeks 20-24)_ - Finalize the adaptive heating curve optimization algorithm based on validation findings. - Prepare master thesis for dissemination and graduation. - Organize a final presentation to showcase project outcomes. - Conduct a review to assess achievements, lessons learned, and areas for future improvement. - Compile final project deliverables technical reports, and software documentation. - Archive project data developed algorithm and documentation for future reference and replication. - Celebrate project success 😊 **Requirement:** - We are seeking a master's student with experience in machine learning or optimization, who is enthusiastic about contributing to a project with practical applicability. - Prior knowledge in Gaussian Process (GP) and Bayesian Optimization (BO) is not expected but will be part of your literature studies. - Basic knowledge in python is mandatory and nice to have are conceptual ideas of digital twin and building energy simulation programs e.g. EnergyPlus. - Students need to find a supervisor at their home university.

If this thesis looks interesting reach out to us and let’s chat about your background and explore how your skills align with our project: michael.locher@empa.ch, alessandro.tell@empa.ch

This study, associated with the Euthermo Project (https://www.sairop.swiss/projects/liste/scalable-deep-reinforcement-learning-algorithms-for-building-climate-control-and-energy-management), focuses on leveraging data-driven methodologies to model the thermal behavior of buildings. The primary objective is to explore the transferability of a building temperature simulation model to other buildings. This involves comparing existing models, developing embedding methods and transfer learning techniques, as well as designing relevant evaluation metrics. Retraining a model from scratch for each new building is unsatisfactory. This project's innovative approach aims to enhance the scalability of the modeling process, thereby reducing the time and resources required compared to the usual practices of training separate models for each building. The overarching goal is to contribute to improved energy efficiency in buildings within the broader context of the global energy transition.

- Conduct a concise comparison of building thermal models, encompassing literature algorithms such as RC model, ARX, PCNN, and other relevant techniques. - Develop a data-driven algorithm for building modeling that can be effectively transferred to new buildings, eliminating the need for completely retraining the model for each unit. - Evaluate the proposed methodology using data associated with the NEST building located in Dübendorf, Switzerland.

mina.montazeri@empa.ch

A major challenge in incorporating a money-free karma system into a monetary crypto-system will be the distributed accounting of karma, and the robustness to so-called Sybil attacks where anonymous users create new accounts to gain karma. Most importantly, how can a solution be derived that is resistant to Sybil attacks whilst retaining some level of decentralisation? With this in mind, your task will be to a) review the state of the art in crypto-governance, b) understand the current karma mechanism, c) develop a tractable game-theoretical formulation for a karma-based crypto-voting scheme, d) demonstrate the efficacy of your approach in simulation experiments, e) identify and devise countermeasures for vulnerabilities such as sybil attacks, and e) (optional) implement and showcase your solution on a real blockchain.

Please apply directly to the posting on SiROP. We look forward to your application. Ezzat Elokda (elokdae@ethz.ch), Aida Manzano Kharman (aida.manzano-kharman17@imperial.ac.uk)

Please see the attached file.

The goals of this project are as follows. - Learn about feedback optimization and robust optimization; - Design a feedback optimization algorithm that is robust against model mismatch and random disturbances; - Characterize the theoretical performance (e.g., optimality and stability) of the closed-loop interconnection of the algorithm and the plant; - Conduct numerical experiments to illustrate the performance of the algorithm. **Publications**: If the final results are promising, we will aim for publication in top-tier control conferences or journals.

Please send your resume/CV (including lists of relevant publications/projects) and transcript of records in PDF format via email to Zhiyu He (zhiyhe@ethz.ch), Dr. Keith Moffat (kmoffat@ethz.ch), and Dr. Saverio Bolognani (bsaverio@ethz.ch). We look forward to your application.