PhD Position Efficient Online Optimization for Time-Varying Systems

Delft University of Technology (TU Delft); Delft; On-site; EUR 36,000 - 45,000; Full time

Benefits offered by this job

Holiday allowance

End-of-year bonus

Relocation assistance

Customisable compensation

Health insurance discounts

Work costs contribution

Flexible work schedules

Job summary

Delft University of Technology invites applications for a PhD position focused on safe learning control for time-varying systems. The project aims to develop computationally efficient algorithms that learn while guaranteeing safety in energy-intensive applications.

You will work with the Delft Center for Systems and Control and collaborate with partners in Switzerland, with a starting date by March 30, 2027, in an environment that supports early career researchers.

Qualifications

• MSc degree in Systems and Control, Mathematics, Computer Science or similar field.
• Prior experience with optimization algorithms.
• Prior experience with Bayesian optimization.
• Prior experience with numerical methods.
• Strong collaboration, communication and writing skills.

Responsibilities

• Develop safe learning methods for time-varying control problems.
• Validate algorithms through experimental collaboration and simulations.
• Contribute to theory and practical applications in energy systems and buildings.

Skills

Optimization algorithms

Bayesian optimization

Numerical methods

Collaboration & writing

Education

MSc degree in Systems and Control, Mathematics, Computer Science or similar

Job description

Job description

How to avoid learning too much - safe and efficient learning for control

Inefficient operation of industrial systems, electricity production, and heating systems in buildings is currently responsible for over 50% of overall greenhouse gases emissions. Improving energy efficiency of energy-intensive sectors by implementing advanced control systems has the potential to both improve efficiency and reduce the costs, all while continuing to meet safety requirements. The flexibility of safe and adaptive learning control methods makes them crucial for safety-critical applications. A key difficulty in these learning-based approaches lies in the fact that the algorithm must simultaneously learn the model and guarantee safety. This dual requirement significantly impacts the computational cost of the learning algorithm, particularly in situations where the optimal decision evolves over time. We now aim to develop computationally efficient safe learning methods for time-varying optimal control problems.

Existing safe learning algorithms usually assume that they have unlimited access to the experiments and thus unlimited new data. Approaches using limited data sets have been restricted to Bayesian optimization without safety constraints. Moreover, the algorithms typically rely on the repetitive nature of the experiments, for instance in robotics or precision manufacturing. The unlimited data combined with repetitiveness limit the applicability of safe learning algorithms in problems evolving over time, such as residential buildings. This PhD project will explore the connection between the dynamics of the optimized system with numerical properties of optimization algorithms to provide a framework for safe learning with minimal interactions over time. Sitting at the intersection of theory and applications, the project includes experimental validation of the developed algorithms in collaboration with Dr Giulia de Pasquale from TU Eindhoven (laboratory tests) and partners in Switzerland (Next Evolution in Sustainable Building Technologies (NEST) residential building).

The PhD would join the group of Dr Marta Zagorowska at Delft Center for Systems and Control (DCSC).

The starting date is no later than the 30th of March 2027.

Job requirements

The ideal candidate will be an ambitious, proactive, reflective, and results-driven researcher. We are looking for the following:

• MSc degree in Systems and Control, Mathematics, Computer Science or similar field (essential)
• Prior experience with optimization algorithms (essential)
• Prior experience with Bayesian optimization (desirable)
• Prior experience with numerical methods (desirable)
• Strong collaboration, communication and writing skills

TU Delft (Delft University of Technology)

Working at TU Delft means contributing to solutions that really make a difference.

For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.

At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means join an international community of professionals and students. Together, we create knowledge, innovations, and solutions that help move the world forward.

Faculty Mechanical Engineering

From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the‑art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well‑being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits

• Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from€3204 - €4051 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours).
• 8% holiday allowance.
• End-of-year bonus of 8.3%.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline‑related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

Additional information

For more information about this vacancy, please contact Dr Marta Zagorowska: [e-mail].

Application deadline: 23 November 2026.