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Post-doc: Machine Learning in Optimisation

We are looking for an innovative post-doctoral researcher to join a cross-disciplinary project funded by NWO, entitled “Real-time data-driven maintenance …

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Mekelweg, Delft, Zuid-Holland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
38 uur
€ 2588 - € 4084 per maand


We are looking for an innovative post-doctoral researcher to join a cross-disciplinary project funded by NWO, entitled “Real-time data-driven maintenance logistics”, which includes several industrial partners such as NS, Philips, and Fokker. This position is an opportunity to gain experience in advanced software development and research and to collaborate with an industrial partner.

The goal of the project is to transition from traditional static maintenance logistics plans to dynamic maintenance logistics policies fuelled by real-time data. Your job will be to investigate different learning algorithms and how to integrate these into the objective functions used in optimisation of maintenance plans. You will implement new learning algorithms that aim to maximise decision-making performance measures such as plan quality instead of traditional measures such as accuracy and predictive performance. These improved learning methods will be integrated into a newly developed real-time decision making framework for maintenance logistics, and published in internationally leading conferences and journals in machine learning, planning and scheduling, and optimisation.


You have:
• a PhD in computer science, mathematics, operations research, or a similar field.
• a keen interest in the combination of machine learning and optimisation.
• a strong motivation for solving real world problems.
• an outstanding research and publication record.
• good analytical and problem solving skills.
• an excellent command of spoken and written English.


The TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

For more information about this position, please contact Sicco Verwer, phone: +31 (0)6-18781180, e-mail: s.e.verwer@tudelft.nl. To apply, please fill in the form at https://goo.gl/forms/NNR8V7TG6yqa67go2

Additional information

Sicco Verwer
+31 (0)6-18781180

Technische Universiteit Delft


Delft University of Technology (the TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. The TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At the TU Delft you will work in an environment where technical sciences and society converge. The TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

Faculty Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) is known worldwide for its high academic quality and the social relevance of its research programmes. The faculty’s excellent facilities accentuate its international position in teaching and research. Within this interdisciplinary and international setting the faculty employs more than 1100 employees, including about 400 graduate students and about 2100 students. Together they work on a broad range of technical innovations in the fields of sustainable energy, telecommunications, microelectronics, embedded systems, computer and software engineering, interactive multimedia and applied mathematics.

Research in the Algorithmics group concerns the development of new algorithms within the field of artificial intelligence. We contribute to the scientific state of the art in methods for automated planning and scheduling and improve upon existing techniques such as reinforcement learning, Markov decision processes, and mathematical programming. We aim to integrate our results in working prototypes and toolboxes to be used by other researchers and industry. Furthermore, we develop game-theoretical mechanisms which govern the interaction between multiple self-interested systems or users.