[jobs] Postdoc at TU Delft on applied planning & scheduling under uncertainty

[Neil Yorke-Smith]

*Postdoc in Applied Planning and Scheduling under Uncertainty*
TU Delft, The Netherlands - deadline 31 March


This two-year post-doctoral position makes fundamental advances in
infrastructure planning and scheduling under uncertainty. These
advances will be applied to modelling of supply chain scenarios for
offshore wind farm installation, including optimised planning schemes.

Researchers at TU Delft have developed design approaches that account
for many different perspectives in major infrastructure projects
including offshore wind farms. These perspectives include manufacturing
of parts, storage and transport capacity limitations, port congestion,
and strong dependence on weather conditions. You will further develop
and push towards application such approaches, to support the development
of supply chain scenarios for wind farm installation. You will lead
optimisation of planning schemes for several practical cases, including
the development of decision rules for the mitigation of disruptions.

Relevant approaches or experience may include planning and scheduling
under uncertainty, reinforcement learning, (distributionally) robust
optimisation, uncertainty quantification, multi-objective optimisation,
and evolutionary algorithms.

The successful applicant will join the STAR Lab at the Faculty of
Engineering, Mathematics and Computer Science, working with Sequential
Decision Making group in the same faculty, and be connected to
researchers in the Faculty of Civil Engineering and Geosciences.

This research is part of the project 'Efficient From Fabrication to
Installation of Large Offshore Assembled Turbines' funded by the
Netherlands Enterprise Agency (RVO). As part of the project, you will
work with industrial partners including Boskalis.

The position is open until filled; applications by *31 March 2025* will
receive full consideration.

Details and to apply: https://careers.tudelft.nl/job/Delft-Postdoc-in-
Applied-Planning-and-Scheduling-under-Uncertainty-2628-CD/814890902/

Informal enquiries may be addressed to: Neil Yorke-Smith <n.yorke-
sm...@tudelft.nl> and Matthijs Spaan <m.t.j...@tudelft.nl>. Please
do not send applications directly by email.


--
Neil YORKE-SMITH
STAR Lab, TU Delft, NL
starlab.ewi.tudelft.nl

[Neil Yorke-Smith]

*Postdoc in Applied Planning and Scheduling under Uncertainty*

TU Delft, The Netherlands - deadline 5 January


This post-doctoral position focuses on developing fundamental
algorithmic advances for dynamic planning and scheduling in multi-
objective supply-chain management. The emphasis is on uncertainty-aware
algorithmic innovation, applied to the complex logistics of offshore
energy supply chains. These supply chains involve tightly
interdependent stages, and strong sensitivity to environmental and
weather conditions.

In the current state of the art, multi-objective planning and scheduling
must integrate diverse and often conflicting decision perspectives.
This creates a growing need for decision-support models grounded in
effective, efficient and scalable algorithms, including advanced AI and
machine learning approaches.

In this 24 month position, you will design, implement and mature such
algorithmic methods, moving them towards practical use in offshore wind-
farm installation planning. You will lead research on scaleability,
dynamic re-planning, and disruption-aware control strategies within
complex, uncertain supply-chain environments. The specific
methodological direction is open and will be shaped collaboratively with
both academic researchers and industrial partners.

The successful applicant will join the STAR Lab at TU Delft's Faculty of
Engineering, Mathematics and Computer Science, working closely with the
Sequential Decision Making Group in the same faculty. As part of this
project, you will collaborate directly with industrial partners such as
Boskalis, with your time divided between TU Delft's campus and the
Boskalis office in Papendrecht.

Applications by *5 January 2026* will receive full consideration; online
interviews are expected the weeks of 12 and 19 January 2026.