Postdoc Integrated Optimization- and Learning-based Multi-agent Control of Large-scale Networks with Hybrid Dynamics

Updated: about 2 months ago
Deadline: 15 Feb 2024

In this postdoc project we will develop integrated optimization-based and learning-based control methods for large-scale hybrid systems – in particular network systems with piecewise affine (PWA) dynamics. More specifically, the aim is to develop innovative approaches to combine model predictive control (MPC) and reinforcement learning so as to merge the advantages of both approaches, and to embed them in a distributed/multi-agent control setting. The key challenge will be to determine efficient approaches to obtain coordination among the control agents. We will also investigate performance-related topics such as stability, computational complexity, error bounds, formal or probabilistic performance guarantees, robustness, finite-termination effects, safety, etc.

Applications for the case studies include multi-modal intelligent transportation networks and smart multi-energy networks.

The postdoc project has a distinct fundamental flavor and is part of the European ERC Advanced Grant project CLariNet – a novel control paradigm for large-scale hybrid networks. The goal of CLariNet is to create a novel paradigm for control of large-scale networks with hybrid dynamics by bridging the gap between optimization-based control and learning-based control. The breakthrough idea is to bridge that gap by using piecewise affine models and to unite the optimality of optimization-based control with the on-line tractability of learning-based control.

The postdoc will join our machine learning and optimization team at the Delft Center for Systems and Control (DCSC) of Delft University of Technology. At the DCSC, our mission is to conduct foundational research in systems dynamics and control, involving dynamic modeling, advanced control theory, and optimization with societally important application fields including energy, transportation, and sustainability.

We offer the opportunity to do scientifically challenging research in a multi-disciplinary research group. 

This position is perfect for you if you have a PhD degree in systems and control, computer science, AI, applied mathematics, or a related field, and with a strong background in optimization-based control and machine learning, in particular reinforcement learning. You are also expected to work on the boundary of several research domains. A good command of the English language is required.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (salary indication: € 4.036 - € 5.090 per month gross). The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme  for partners and they organise events to expand your (social) network.

The position is a temporary assignment for up to 24 months.

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values  and we actively engage  to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the 3mE faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

3mE is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in 3mE’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Click here  to go to the website of the Faculty of Mechanical, Maritime and Materials Engineering. Do you want to experience working at our faculty? These videos  will introduce you to some of our researchers and their work.

For more information about this vacancy, please contact Bart De Schutter, [email protected] .

The position can either be a full-time one, or if the successful candidate requests it, a part-time one (80% or higher). In accordance with the equal opportunity policy of Delft University of Technology female candidates are in particular encouraged to apply.

Are you interested in this vacancy? Please apply by 8 January 2024, via the application button and upload your letter of application along with a detailed curriculum vitae, a motivation why the proposed research topic interests you, a list of publications, (electronic) copies of your three most relevant journal or conference publications, the abstract and/or summary of your PhD thesis, your MSc course program and the corresponding marks, names and addresses of three reference persons, and all other information that might be relevant to your application. 

For information about the application procedure, please contact Irina Bruckner, our HR advisor at [email protected] .

Please note:
- A pre-employment screening can be part of the selection procedure.
- You can apply online. We will not process applications sent by email and/or post.
- Please do not contact us for unsolicited services.

Similar Positions