PhD: Fair Distributed Optimization in Large-Scale Networks

Eindhoven University of Technology; Eindhoven; On-site; EUR 36,000 - 45,000; Full time

Job summary

Eindhoven University of Technology (TU/e) invites applications for a PhD focused on fair distributed optimization and control of large-scale networked systems. You will design scalable multi-agent algorithms for congestion management in electricity distribution networks, embedding fairness into distributed control with provable guarantees.

You will work in the Control Systems group, collaborating with experts in distributed control, responsible AI and power systems, testing on real-world data

Qualifications

• Master’s degree or equivalent in a related field.
• Strong background in control theory and/or optimization.
• Solid mathematical skills and a research mindset.
• Fluent in English (C1).

Responsibilities

• Formulate fair resource allocation problems for large-scale, distributed systems.
• Develop and analyze distributed optimization algorithms (consensus-based, primal-dual, ADMM, game-theoretic).
• Design fairness-aware feedback controllers ensuring scalability and stability.
• Analyze probabilistic allocation policies and uncertainty using stochastic/robust methods.
• Publish results in top venues and collaborate with power systems and AI experts.

Skills

Control theory

Optimization

Applied math

Multi-agent

Python

MATLAB

Julia

Interdisciplinary teamwork

Education

Master's in Systems & Control

Electrical Engineering

Applied Mathematics

Operations Research

Tools

None

Job description

Eindhoven University of Technology (TU/e) invites applications for a PhD focused on fair distributed optimization and control of large-scale networked systems. You will design scalable multi-agent algorithms for congestion management in electricity distribution networks, embedding fairness into distributed control with provable guarantees.

You will work in the Control Systems group, collaborating with experts in distributed control, responsible AI and power systems, testing on real-world data