Doctoral Student - Enhancing Building Efficiency through Edge-based Machine Learning and Control
100%, Zurich, fixed-term
We are looking for a doctoral student to join our international team and contribute to our research efforts in the area of control and automation for energy systems. The doctoral student will be co-supervised by Dr. Efe Balta (inspire AG) and Prof. John Lygeros (IfA).
The Automatic Control Laboratory (IfA) in the Department of Information Technology and Electrical Engineering of ETH Zurich is a community of approximately 50 researchers from more than 20 countries working on the development of methods and computational tools for automation, exploring their potential for promoting our social well-being in areas such as energy systems, transportation, and industrial processes.
inspire AG is the leading Swiss competence centre for product innovation and advanced manufacturing. As a strategic partner of ETH Zurich, our mission is to transfer knowledge and technology from research to Swiss machine, electrical and metal industries.
Project background
Modern buildings operate thanks to a complex network of sensors and control systems at various levels, that manage tasks such as air quality control, temperature regulation, and zone-specific environmental conditioning. Advanced control methods can significantly improve building efficiency and reduce emissions. A significant challenge is the limited computational capability of edge devices in buildings coupled with the complex interconnections and time varying dynamics. Addressing these challenges requires the development of the next generation of efficient data-driven methods that can adapt to changing external conditions and continuously learn from data in a distributed way. Coupling of new theory with effective implementation strategies have the potential to make a lasting impact on building efficiency through sustainable automation.
Job description
The main goal of your work is to develop new theory and methods in the field of distributed data-driven control. Specific emphasis will be given to the development of computationally efficient data-driven control and machine learning methods that enable deployment on edge devices with limited computation.
We are looking for a motivated doctoral student to contribute to this effort. The envisioned research will tackle:
Developing theory for centralised and distributed data-driven control strategies
Analysis of data-driven methods to include prior information about the underlying system
Addressing process specific nonlinearities and system-theoretic properties to develop novel control and machine learning algorithms for edge computing
Active collaboration with our industrial partner in the HVAC sector, to test the developed methods on real systems and identify novel problems to address.
Profile
You are highly motivated and dedicated with a master's degree in electrical, mechanical, or industrial engineering. You are driven by an interest in developing novel theory and methods for solving real-world problems. Programming, modelling, and data analysis skills in python and machine learning/optimisation libraries/toolboxes support you in contributing to our ongoing software development efforts. Your spoken and written English skills help you navigate our international environment.
Workplace
Workplace
We offer
We offer a full-time doctoral position at the ETH Automatic Control Lab, a multifaceted, modern research environment with excellent infrastructure. Through the close cooperation with inspire AG and our industrial project partner, you will quickly establish contacts to industry. The research project is at the intersection of control and machine learning theory with a strong emphasis on real-world applicability.
We value diversity
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.
Curious? So are we.
We look forward to receiving your application including the following documents
A short statement of research interests and objectives.
A CV including past research work and projects.
2-3 reference letters/contacts.
One publication/thesis.
Transcripts of all degrees in English.
Please note that we only accept applications submitted through the online application portal. Applications sent via email or postal services will not be considered.
Please submit all information as a single merged PDF file, titled as last name and the date of application. For example, lastname_ PDF.
The positions is available immediately and will remain open until filled. Applications received by 15 October 2025 will receive full attention.
Further information about the Automatic Control Lab and inspire AG can be found under our websites and