PhD in AI-driven Fair Energy Curtailment Policies

Are you interested in tackling one of the most urgent challenges of the energy transition and pushing the boundaries of AI integrated distributed optimization for largescale energy networks As a PhD candidate you will help design AIdriven control algorithms that allow thousands of distributed energy resources to coordinate locally while ensuring the grid remains stable efficient and fair. You will explore probabilistic curtailment policies develop scalable multiagent decisionmaking methods and work with realworld data to create solutions that can be deployed in tomorrows electricity systems. This position offers a unique opportunity to shape both cuttingedge theory and real societal impact.

The rapid growth of Distributed Energy Resources (DERs) such as rooftop photovoltaics (PV) is transforming electricity distribution networks. While essential for decarbonization this transition creates new operational challenges. Local electricity networks can become congested when many producers inject power simultaneously or due to electrification of consumption such as heat pumps (HPs) and electric vehicles (EVs). This project aims to develop fair and reliable AI-based control methods for managing congestion in electricity distribution networks. The core idea is to embed clear and interpretable notions of fairness directly into the algorithms that determine how much energy different prosumers may inject during congestion.

The proposed project aims to advance fundamental science at the intersection of electrical power engineering control machine learning and sociotechnical design. It seeks to redefine how societal and economic objectives are formalized and enforced in intelligent control systems. Achieving this requires close interdisciplinary collaboration.

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 collaborate closely with experts in responsible AI energy systems and distributed control working across departments in an interdisciplinary team.

Your work will directly contribute to a fairer more resilient and more sustainable energy system. By developing algorithms that help DSOs manage congestion transparently and equitably you support the acceleration of renewable energy integration and strengthen public trust in the energy transition.

• Review literature on congestion management: Study stateoftheart methods for mitigating grid congestion with a focus on proactive and distributed control strategies in modern distribution networks.
• Analyze distributed optimization methods: Investigate existing distributed and decentralized optimization algorithms (e.g. consensusbased ADMMtype multiagent MPC) and assess their suitability for largescale distributed energy resources coordination.
• Develop probabilistic curtailment policies: Explore rafflebased and weighted probabilistic allocation schemes grounded in fairness theory and translate them into implementable control rules.
• Design fairnessaware distributed controllers: Formulate distributed control algorithms that integrate fairness constraints while ensuring scalability stability and robustness.
• Model uncertainty in renewable generation: Incorporate stochastic models of PV output demand fluctuations and network variability into the control framework.
• Evaluate robustness of control policies: Assess how the proposed algorithms perform under forecasting errors model mismatches and communication delays.
• Implement multiagent coordination mechanisms: Develop local decisionmaking rules that allow DERs to coordinate with minimal communication while achieving global fairness and efficiency.
• Simulate largescale distribution networks: Use realistic feeder models and open datasets to test algorithmic performance under diverse operating conditions.
• Collaborate with energy system experts: Work closely with colleagues in control AI and electrical engineering to align algorithmic design with realworld grid constraints.
• Validate methods on realworld data: Apply the developed algorithms to datasets from Dutch DSOs to ensure practical relevance and societal impact.

This PhD position is part of the project Responsible AI for Fair and Efficient Control of Energy Systems (RAICES) ( Requirements

• A masters degree (or an equivalent university degree)in Electrical Engineering Mechanical Engineering Mathematics Physics.
  • In case you are still doing your MSc in the field listed above please indicate your expected graduation date in the cover letter.
• 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).

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 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. 3059 - max. 3881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• 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 on-campus childrens day care and sports facilities.
• Unlimited access to the modern oncampus TU/e Student Sports Center at an exceptionally affordable rate.
• An allowance for commuting working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact today and in the future. TU/e is home to over 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven a worldleading tech region with more than 7000 hightech companies and strong R&D activity. Known for breakthroughs in AI photonics semiconductors and advanced manufacturing Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

The mission of the Department of Electrical Engineering is to acquire share and transfer knowledge and understanding in the whole field of Electrical Engineering through education research and valorization. We work towards a Smart Sustainable Society a Connected World and a healthy humanity (Care & Cure). Activities share an application-oriented character a high degree of complexity and a large synergy between multiple facets of the field.

Research is carried out into the applications of electromagnetic phenomena in all forms of energy conversion telecommunication and electrical signal processing. Existing and new electrical components and systems are analyzed designed and built. The Electrical Engineering department takes its inspiration from contacts with high-tech industry in the direct surrounding region and beyond.

The department is innovative and has international ambitions and partnerships. The result is a challenging and inspiring setting in which socially relevant issues are addressed.

Do you recognize yourself in this profile and would you like to know more Visit our website for more information about the application process. You can also contact Dr. Giulia De Pasquale () or Dr. Nikolaos Paterakis ().

Curious to hear more about what its like as a PhD candidate at TU/e Please view the video.

Are you inspired and would like to know more about working at TU/e Please visit our career page.

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.
• A copy of the MSc thesis if written in English or a 2-pages summary otherwise.

Ensure that you submit all the requested application documents. Please note that incomplete applications may not be considered and could be rejected.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.