PhD in Fair Distributed Optimization and Control of Large-Scale Networked Systems

AcademicTransfer; Nederland; On-site; EUR 36,000 - 45,000; Full time

Benefits offered by this job

Pension scheme

Extended leave

Holiday allowance

Year-end bonus

Commuting allowance

On-campus childcare

Mental health support

Job summary

Eindhoven University of Technology (TU/e) invites applications for a PhD position in distributed optimization and control theory within the Control Systems group. The project focuses on scalable, fair resource allocation in large-scale networked systems, with emphasis on convergence guarantees and real-world data from Dutch networks.

The successful candidate will develop and analyze algorithms (consensus-based, primal-dual, ADMM-type) and probabilistic allocation policies, and will publish in

Qualifications

• Master’s degree in a relevant field or equivalent.
• Strong background in control theory and/or optimization.
• Strong mathematical and analytical skills.
• Fluent in English (spoken and written).
• Programming experience in Python, MATLAB or Julia.

Responsibilities

• Formulate fair resource allocation problems for large-scale multi-agent systems.
• Design and analyze distributed optimization algorithms.
• Develop fairness-aware feedback controllers with provable guarantees.
• Validate methods on realistic networked systems and data.

Skills

Control theory

Optimization

Mathematical skills

Teamwork

English

Education

Master's degree in Systems & Control / related field

Tools

Python

MATLAB

Julia

Job description

Eindhoven University of Technology (TU/e)

Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering > Control engineering Researcher Profile First Stage Researcher (R1) Application Deadline 4 Dec 2026 - 22:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Can thousands of autonomous agents, each with only local information, reach decisions that are optimal, stable and provably fair? In this PhD you will build the distributed optimization and control theory that answers this question. You will design scalable multi-agent algorithms with fairness built in, handle uncertainty with stochastic and robust methods, and prove convergence, stability and fairness guarantees. Your testbed will be one of the largest and most demanding networked systems there is: the electricity distribution grid, where thousands of solar panels, heat pumps and electric vehicles compete for limited network capacity. If you enjoy turning hard real-world problems into elegant mathematics, this position is for you.

Note to applicants: No background in energy systems is required; you will pick up the domain knowledge during the project, together with our energy experts. We are looking for candidates with a strong background in control systems, optimization or applied mathematics.

Information

At its core, this project tackles a fundamental problem in networked control: how to allocate a scarce shared resource among many agents in a way that is efficient, scalable and fair, using only local computation and limited communication. The motivating application is congestion management in electricity distribution networks. As rooftop photovoltaics, heat pumps and electric vehicles spread, local networks increasingly hit their capacity limits, and operators must decide how much power each prosumer may inject or consume. The core idea is to embed clear and interpretable notions of fairness directly into distributed control and optimization algorithms, as constraints or objectives with provable guarantees.

The project advances fundamental science at the intersection of distributed optimization, control theory, multi-agent systems and machine learning. It seeks to redefine how societal objectives such as fairness are formalized mathematically and enforced in intelligent control systems.

You will be embedded in the Control Systems group at TU/e, contributing to its research program on intelligent and responsible control of networked systems.

You will work closely with experts in distributed control, responsible AI and power systems across departments, who provide the domain knowledge and real-world data to test your methods.

Beyond the theory, your algorithms will help distribution system operators manage congestion transparently and equitably, speeding up the integration of renewable energy and strengthening public trust in the energy transition.

As a PhD candidate, you will:

• Formulate fair resource allocation problems: Cast congestion management as a large-scale, constrained multi-agent optimization problem, and formalize interpretable fairness notions (e.g., proportional, max-min or probabilistic fairness) as mathematical constraints or objectives.
• Design distributed optimization algorithms: Develop and analyze consensus-based, primal-dual, ADMM-type and game-theoretic algorithms that coordinate thousands of agents using only local information and limited communication.
• Develop fairness-aware feedback controllers: Design distributed controllers (e.g., distributed MPC, online feedback optimization) that enforce fairness while guaranteeing scalability, stability and constraint satisfaction.
• Analyze probabilistic allocation policies: Study randomized and weighted allocation schemes, such as raffle-based curtailment, and characterize their fairness and efficiency over time.
• Handle uncertainty: Incorporate stochastic models of generation and demand using tools from stochastic, robust and learning-based control.
• Prove theoretical guarantees: Establish convergence, stability, robustness and fairness guarantees, including under forecasting errors, model mismatch and communication delays.
• Validate on realistic large-scale networks: Test your algorithms in simulations of realistic distribution networks and on real-world data from Dutch distribution system operators.
• Publish and collaborate: Publish in leading control and optimization venues (e.g., IEEE TAC, Automatica, CDC) and work with colleagues in control, AI and electrical engineering.
• A master’s degree (or an equivalent university degree) in Systems and Control, Electrical Engineering, Mechanical Engineering, Applied Mathematics, Operations Research or a related field.
• In case you are still doing your MSc in the field listed above, please indicate your expected graduation date in the cover letter.
• A strong background in control theory and/or optimization (e.g., convex or distributed optimization, multi-agent systems, model predictive control, game theory).
• Solid mathematical skills and enjoyment of rigorous analysis and proofs.
• Programming experience (e.g., Python, MATLAB or Julia).
• A research-oriented attitude with curiosity.
• Ability to work in an interdisciplinary team.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Prior knowledge of power or energy systems is not required.

Additional Information

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.

In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities , scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
• In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
• Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
• As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, and on-campus children's day care.
• We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
• An allowance for commuting, working from home and internet costs.

Do you recognize yourself in this profile and would you like to know more? Please contact Dr. Giulia De Pasquale ( [e-mail] ) or Dr. Nikolaos Paterakis ( [e-mail] ).