PhD / Postdoc on Model Predictive Control for Energy Management

With households accounting for more than 25% of the overall energy consumption in the European Union, effective home energy management is key to reducing costs, lowering emissions, and supporting grid stability. In this project, you will design MPC strategies that scale from single households to interconnected residential energy hubs. You will explore three cutting-edge directions: (1) AI-enhanced distributed MPC to improve computational speed, (2) cooperative approaches enabling dynamic collaboration among households, and (3) quantum-based MPC to potentially accelerate optimization tasks. Your work will address pressing challenges in scalability, cyber-security, and real-time operation, paving the way for practical deployment of sustainable MPC-based energy management systems.

Additionally, you will have access to the graduate courses at the Dutch Institute of Systems and Control (DISC) and have the opportunity to collaborate with industrial and academic partners. You will partake in teaching duties at the bachelor and/or master's levels, collaborate in the supervision of master’s degree projects, and attend leading conferences in the systems and control field. By joining us, you will be part of the Control Systems Technology Section, a vibrant community of more than 80 researchers including faculty members, postdocs and PhDs working on diverse fundamental and application-oriented topics in the fields of control and systems theory, cyber-physical systems, optimization, artificial intelligence, machine learning, and systems engineering.

PhD / Postdoc on Model Predictive Control for Energy Management

Introduction
We invite highly motivated students with a strong background in systems and control to apply for a PhD/Postdoc position within the Control Systems Technology section at the Department of Mechanical Engineering. This project focuses on developing model predictive control (MPC) algorithms for residential energy management systems and energy hubs, with particular emphasis on distributed optimization, cyber-security, and quantum computing for real-time decision-making. Ultimately, the goal is to equip households and neighborhoods with smart energy management tools that make everyday living more sustainable.

Job Description
With households accounting for more than 25% of the overall energy consumption in the European Union, effective home energy management is key to reducing costs, lowering emissions, and supporting grid stability. In this project, you will design MPC strategies that scale from single households to interconnected residential energy hubs. You will explore three cutting-edge directions: (1) AI-enhanced distributed MPC to improve computational speed, (2) cooperative approaches enabling dynamic collaboration among households, and (3) quantum-based MPC to potentially accelerate optimization tasks. Your work will address pressing challenges in scalability, cyber-security, and real-time operation, paving the way for practical deployment of sustainable MPC-based energy management systems.

Additionally, you will have access to the graduate courses at the Dutch Institute of Systems and Control (DISC) and have the opportunity to collaborate with industrial and academic partners. You will partake in teaching duties at the bachelor and/or master's levels, collaborate in the supervision of master’s degree projects, and attend leading conferences in the systems and control field. By joining us, you will be part of the Control Systems Technology Section, a vibrant community of more than 80 researchers including faculty members, postdocs and PhDs working on diverse fundamental and application-oriented topics in the fields of control and systems theory, cyber-physical systems, optimization, artificial intelligence, machine learning, and systems engineering.

Job Requirements
We are looking for talented, enthusiastic PhD candidates with a master’s degree in Mechanical or Electrical Engineering, Systems and Control, Applied Physics, or Applied Mathematics. This requirement is mandatory.

In addition, the ideal candidate would have the following experience, skills and interests:

• Strong background in energy systems and/or optimization.
• Ability to balance theoretical work with applied work, taking ownership of the research project.
• Interest in collaborating with an international team of academic and industrial partners.
• Motivated to develop your teaching skills and coach students.
• Experience with programming in Matlab or Python.
• Fluent in spoken and written English (C1 level).

Conditions of Employment
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:

for a PhD:

• Full-time employment for 4 years (fully funded), 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. € 3,059 max. € 3,881).

for a PostDoc:

• Full-time employment for 2 years.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale ... (min. € 4,241 max. € 5,538).
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.

and for both:

• 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 children's day care and sports facilities.
• 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.

Information
The Control Systems Technology Section is among the largest and high-impact research centers in systems and control. It is uniquely positioned in a key high-tech region, the Brainport Eindhoven (http://brainporteindhoven.com), which is home to many leading companies. As such, it is a unique section in which both state-of-the-art fundamental and applied research is performed and deeply intertwined. High-quality research and high-quality education are two sides of the same coin, and we educate many engineers for Dutch and international industries and universities.

Do you recognize yourself in this profile and would you like to know more? Please contact Assistant Prof. Paula Chanfreut, p.chanfreut.palacio@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.gemini@tue.nl.

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

Application
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.
• Brief description of your MSc thesis and/or relevant research projects.

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.

PhD on AI-driven Repair Recommendations for Sustainable Manufacturing

Introduction
Are you passionate about developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy.

Job Description
In the manufacturing landscape, determining whether a component should be repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and optimize repair decisions.

You will develop novel data-centric approaches to Remaining Useful Life (RUL) prediction that goes beyond traditional model-focused methods. You will focus on understanding and improving how data is collected, managed, and utilized throughout the entire process. Your research will encompass developing machine learning techniques that thrive with imperfect data, creating adaptive models that can quickly learn from new machines with minimal training data, and integrating these predictions with optimization algorithms to make cost-effective and environmentally sustainable decisions about component lifecycle management.

You will be part of the large ADD-reAM project, an NWO-funded consortium having 15 PhD researchers exploring complementary aspects of additive manufacturing, including technical design, logistics, sustainability assessment, and regulatory frameworks. Your PhD position will be embedded in the Information Systems group within the Department of Industrial Engineering and Innovation Sciences (IE&IS) at Eindhoven University of Technology (TU/e), collaborating closely with researchers working on predictive maintenance, operational decision-making, and artificial intelligence.

Job Requirements

• A master's degree (or an equivalent university degree) in Computer Science, Machine Learning, Operations Research or a related technical field.
• Strong background in deep learning with a motivation to advance fundamental techniques.
• Excellent analytical, problem-solving, and software engineering skills with prior experience implementing machine learning algorithms using well-known frameworks (e.g., PyTorch).
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop teaching skills and coaching skills.
• Strong communication skills, including proficiency in written and spoken English (C1).

Conditions of Employment
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. € 3,059 max. € 3,881).
• 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 children's day care and sports facilities.
• 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.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact prof.dr.ir. Remco Dijkman (R.M.Dijkman@tue.nl) or dr.ir. Zaharah Bukhsh (z.bukhsh@tue.nl).

Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.IEIS@tue.nl.

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

Application
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.
• Copies of diplomas and academic transcripts with grades from prior university studies.
• MSc thesis (in English) and/or research work.

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.

PhD on AI-driven Repair Recommendations for Sustainable Manufacturing

In the manufacturing landscape, determining whether a component should be repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and optimize repair decisions.

You will develop novel data-centric approaches to Remaining Useful Life (RUL) prediction that goes beyond traditional model-focused methods. You will focus on understanding and improving how data is collected, managed, and utilized throughout the entire process. Your research will encompass developing machine learning techniques that thrive with imperfect data, creating adaptive models that can quickly learn from new machines with minimal training data, and integrating these predictions with optimization algorithms to make cost-effective and environmentally sustainable decisions about component lifecycle management.

You will be part of the large ADD-reAM project, an NWO-funded consortium having 15 PhD researchers exploring complementary aspects of additive manufacturing, including technical design, logistics, sustainability assessment, and regulatory frameworks. Your PhD position will be embedded in the Information Systems group within the Department of Industrial Engineering and Innovation Sciences (IE&IS) at Eindhoven University of Technology (TU/e), collaborating closely with researchers working on predictive maintenance, operational decision-making, and artificial intelligence.

Postdoc in Quantum Optimization for the Energy Transition

Short introduction
Are you passionate about applying quantum computing to addressing real-world challenges? Join us to develop quantum optimization methods that support the transition to sustainable and resilient electrical power systems!

Job Description
The power and energy systems community is increasingly interested in emerging computing technologies, including quantum computing, to address challenges in power system optimization, particularly in the context of the Energy Transition. Transitioning to a power system heavily reliant on weather-dependent renewable energy to achieve environmental targets introduces a critical dimension of uncertainty in power system operations, necessitating more complex and adaptive decision-making processes for system operators. While some prototypical power system optimization problems have been adapted for quantum computing, current hardware limitations restrict scalability and prevent systematic exploration of large-scale, constrained optimization problem instances where genuine quantum advantage may arise.

The focus of this project is to develop techniques that will enable gate-based quantum optimization algorithms to tackle realistic (large-scale, mixed-integer, and constrained) instances of stochastic programming problems used in power system operations under uncertainty in conjunction with classical high-performance computing.

Interesting directions for this project include, but are not limited to:

• Quantum algorithms for constrained optimization
• Mathematical decomposition-based hybrid quantum-classical optimization algorithms with convergence guarantees
• Hardware-efficient encoding of constrained optimization problems

You will primarily contribute to the Intelligent Energy Systems program within the Electrical Energy Systems group.

Job Requirements

• PhD in Electrical Engineering, Applied Physics, Operations Research, Computer Science, or a related discipline, with a focus on quantum algorithms. Knowledge of electrical power systems is a plus, but not required – you can grow into that aspect during the project.
• Availability to start no later than January 2026.
• Ability to conduct high-quality academic research in (hybrid) quantum computing, reflected in demonstrable outputs, for instance, by a relevant PhD thesis and/or publications.
• Proficiency in Python and quantum software development kits (e.g., Qiskit).
• Excellent command of the English language and good communication skills. Note that there is no Dutch language requirement.

Conditions of Employment
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 1 year.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 4,241 max. € 5,538).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs.
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

Information
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager dr. Nikolaos Paterakis, Assistant Professor, n.paterakis@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.flux@tue.nl.

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

Application
We invite you to submit a complete application using the “apply now” button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position. Please indicate your availability.
• Curriculum vitae, including a list of your publications and the contact information of two references.

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.

Applications by e-mail are not accepted!