PhD: Chaotic Sampling for Secure Networked Control

Delft University of Technology

Delft

On-site

EUR 38,000 - 49,000

Full time

5 days ago
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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

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

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