PhD Position Chaotic Sampling for Secure and Sustainable Networked Control Systems

Delft University of Technology; Delft; On-site; EUR 38,000 - 49,000; Full time

Job summary

Delft University of Technology in the Netherlands invites applications for a PhD position on Chaotic Sampling for Secure and Sustainable Networked Control Systems. You will develop mathematical theory and computational methods to analyze chaotic sampling, study conditions for chaos, and design controllers and samplers with stability and efficiency in mind.

You will gain deep insights across nonlinear dynamics, optimization, and theoretical computer science, with opportunities to contribute to

Qualifications

• MSc in systems & control, electrical or mechanical engineering, applied mathematics, physics, CS, or related field.
• Strong passion for mathematics, abstraction, and theory-driven research.
• Curiosity about chaos, nonlinear dynamics and unpredictability.
• Solid foundations in linear algebra and differential equations.
• Proficiency in scientific programming and excellent English.

Responsibilities

• Develop theory and computational methods for chaotic sampling in networked control systems.
• Identify conditions for chaotic behavior and design stability-aware controllers and samplers.
• Balance stability, communication rate and timing unpredictability (entropy).
• Produce open-source software and validate results with simulations.

Skills

Mathematics enthusiasm

Theoretical research

Chaos theory

Nonlinear dynamics

Unpredictability

Linear algebra

Differential equations

Scientific programming

English

Education

MSc in Systems & Control

Job description

PhD Position Chaotic Sampling for Secure and Sustainable Networked Control Systems: Engineers usually want predictability. This project embraces chaos! – Excited about control theory? Then join us to build the math of chaotic sampling for greener and more secure control systems.

Job description

Wireless communication can make control systems cheaper and easier to deploy, but bandwidth and energy limitations, as well as vulnerability to cyber-attacks, restrict its use in critical applications. Event-based control approaches can greatly reduce communication by transmitting information only when required, while potentially generating complex, chaotic sampling patterns. This project investigates how such patterns can be designed systematically to make communication timing difficult for an external observer to predict, while maintaining closed-loop stability and using network resources efficiently.

In this PhD project, you will develop mathematical theory and computational methods for the analysis and design of chaotic sampling mechanisms in networked control systems. You will study conditions under which event-based sampling exhibits chaotic behavior and how to design controller and sampler strategies that account for stability, communication rate, and measures of timing unpredictability such as entropy. You will also investigate the robustness of these mechanisms to disturbances and assess whether transmission patterns can be learned from external observations.

This position is part of a new lab at the Delft Center for Systems and Control, supervised by Gabriel de Albuquerque Gleizer. The research will allow you to gain deep insights across nonlinear dynamics, optimization, and theoretical computer science, and to combine those in new innovative ways. The position will offer you the opportunity to work on important problems with real-world impact and a strong publication potential. As the project progresses, you will also have the freedom to contribute to an experimental demonstration using a wireless lab-scale control system. Still, this position is theory-centric: expected outcomes include theoretical results, mathematical proofs, and computational algorithms, as well as open-source software and validation through simulations and analysis. Experimental demonstration is an option if it interests you.

Teaching activities are part of your PhD trajectory and may include, for example: supervising workgroups or lab sessions, assisting in courses, or mentoring BSc and MSc students. While teaching will not be your main responsibility, it offers valuable experience that supports your development and prepares you for future academic or professional roles. Teaching activities will not exceed 20% of your total appointment, averaged over the course of your PhD.

Job requirements

Essential

• An MSc degree in systems & control, electrical engineering, mechanical engineering, applied mathematics, physics, theoretical computer science, or a closely related field.
• Strong enthusiasm for mathematics and abstraction, matched with the motivation to conduct theory-driven research, the ability to develop rigorous mathematical arguments, and a drive to improve these skills.
• Curiosity about chaos, nonlinear dynamics, and unpredictability.
• Solid foundations in linear algebra and differential equations.
• Proficiency in scientific programming and an excellent command of English.

Advantages (but not required)

• Background in systems and control. For example, familiarity with stability analysis, state-space methods, jump-flow systems, or continuous-time and discrete-time LTI systems theory is a plus.
• Experience with mathematical modeling, optimization, numerical computation, algorithm development, or machine learning.
• Prior knowledge on nonlinear dynamics, dynamical systems theory, networked control systems, event-based control, discrete-event systems, or theoretical computer science is not a requirement, but it is an advantage.

TU Delft (Delft University of Technology)

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

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.

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 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), plus 8% holiday allowance and an 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.

• Hours per week: 38-40.
• FTE: 1,0.

As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed.