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Postdoctoral Researcher in Optimization and Railway Timetabling

university positions • Democratic Republic Of The Congo
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AI Summary

Join the ICAI Robust RAIL Lab to develop trustworthy multi-model optimization pipelines for the Dutch railway network. You will focus on combining macroscopic planning with microscopic simulation and designing market-based capacity allocation mechanisms. Requires a PhD in optimization, operations research, or machine learning with strong mathematical grounding.

Key Highlights
Develop self-certifying optimization proxies to bridge different levels of abstraction in railway timetabling
Design strategy-proof mechanism-design solutions for multi-operator rail capacity allocation
Collaborate with TU Delft, Utrecht University, ProRail, and NS in an industry-connected lab
Key Responsibilities
Combine macroscopic capacity planning, mesoscopic scheduling, and microscopic simulation into a single computationally tractable pipeline.
Develop fast machine learning surrogates with provable optimality guarantees.
Design fair and efficient mechanisms for allocating scarce train paths across competing railway operators.
Collaborate with researchers in the Algorithmics section and industry contacts at ProRail and NS.
Shape a personal research agenda within the scope of the Robust RAIL Lab.
Technical Skills Required
Optimization Operations Research Machine Learning
Benefits & Perks
Salary and benefits per Collective Labour Agreement for Dutch Universities
ABP pension scheme
Individual employment package
Health insurance discounts
Flexible working week
232 annual leave hours
Individual choice budget for leave
Education and training opportunities
Partially paid parental leave
Vitality program
Relocation support via Coming to Delft Service
Dual Career Programme for partners

Job Description


ICAI Robust RAIL Lab is looking for a postdoctoral researcher to help bridge that gap, along two connected directions.

Job Description

The Dutch railway network is approaching its capacity limits, and the timetable that keeps it running is one of the most consequential optimization problems in the country — spanning many orders of magnitude in detail, from macroscopic capacity-planning down to millisecond-level microscopic simulation. No single algorithm spans that whole range on its own.

ICAI Robust RAIL Lab — a joint initiative of TU Delft, Utrecht University, and NS/ProRail under the NWO LTP-ROBUST programme — is looking for a postdoctoral researcher to help bridge that gap, along two connected directions.

  • Trustworthy multi-model optimization. Combine models at different levels of abstraction — macroscopic capacity planning, mesoscopic scheduling, microscopic simulation — into a single, computationally tractable pipeline, without sacrificing trust in the result. Draw on recent work on self-certifying optimization proxies — fast machine learning surrogates that carry their own provable optimality guarantee, falling back to an exact solver only on the instances that need it — and on the broader idea of building a relaxation that's fine-grained exactly where a query needs it and coarse elsewhere. This hybrid, multi-model philosophy motivates decision support in other domains as well.
  • Timetabling for a multi-operator railway. ProRail's system for allocating rail capacity is shifting from an operator-request model to one where train paths are effectively "sold" to multiple, competing railway operators — turning capacity allocation into a genuine market-design problem. How do you allocate scarce train paths fairly, efficiently, and strategy-proofly across operators who each hold private information about their own needs? This is a live mechanism-design question, not a hypothetical one: it echoes the FCC's landmark two-sided spectrum incentive auction.

You'll work with researchers in the Algorithmics section, headed by Mathijs de Weerdt, contacts at ProRail and NS, and some of the lab's researchers (PhD tracks on hub planning, real-time rescheduling, crew rostering, energy-efficient operation) — with freedom to shape your own research agenda within this scope.

What we offer: a postdoctoral position for 16 fte months (working days per week and exact duration negotiable), embedded in a well-resourced, industry-connected lab.

Job Requirements

We're looking for: a recent PhD graduate in optimization, operations research, machine learning, or a closely related field, with strong mathematical/algorithmic grounding and a genuine interest in working at the interface of theory and a large, real-world system for sustainable transport.

Conditions of employment

  • Duration of contract is 16 months. Temporary.
  • A job of 32-40 hours per week.
  • Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
  • An excellent pension scheme via the ABP.
  • The possibility to compile an individual employment package every year.
  • Discount with health insurers on supplemental packages.
  • Flexible working week.
  • Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
  • Plenty of opportunities for education, training and courses.
  • Partially paid parental leave
  • Attention for working healthy and energetically with the vitality program.

Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. .

Additional Information

Interested? Get in touch to discuss the position with Prof. Mathijs de Weerdt [email protected].

Application procedure

Please apply no later than 31 Oct 2026 via the application button and upload the following documents:

  • CV
  • Motivational letter

You can address your application to prof. Mathijs de Weerdt.

Please Note

  • You can apply online. We will not process applications sent by email and/or post.
  • As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
  • Please do not contact us for unsolicited services.

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