The project addresses difficult scheduling and packing problems in the
sense of computational complexity, for which classical exact approaches
are often impractical at realistic scales. The goal is to design new
algorithmic frameworks that combine established tools from Operations
Research with modern Machine Learning methods to produce high-quality
solutions within acceptable computational times. While rooted in OR, the
project requires and will further develop strong competencies in Machine
Learning.
Over the past decade, learning-augmented optimization has emerged as a
promising paradigm. In exact methods, machine learning has been used to
tune solver parameters or guide search in tree-based algorithms for
mathematical programming. In heuristic optimization, learning has
supported diversification strategies, automated selection of algorithms
for specific instances, and even direct construction of solutions. More
recently, reinforcement learning and deep learning techniques have been
used to guide local search procedures, particularly for transportation
and routing problems, demonstrating substantial performance improvements.
This project will focus on scheduling and packing settings, developing
general methodologies rather than problem-specific tricks. Two
complementary research directions will be pursued.
First, machine learning will be used to improve the parameterization of
heuristic algorithms.
Second, learning methods—especially reinforcement learning—will be
employed to guide the exploration of solution spaces. This includes
selecting promising neighborhoods, prioritizing moves in local search,
or constructing solutions incrementally.
The research will build on several successful applications of ML in
combinatorial optimization. These include reinforcement learning–guided
greedy procedures for graph optimization problems, predictive models for
deciding when decomposition techniques should be applied, and
classifiers that identify structural characteristics of high-quality
solutions. Additional directions involve predicting optimal objective
values for complex engineering design problems and developing
reinforcement learning–enhanced metaheuristics, such as iterated local
search for makespan minimization in advanced manufacturing scheduling.
The developed methods will be evaluated on challenging NP-hard
scheduling and packing problems, including both well-studied benchmark
problems with strong existing heuristics and more applied, real-world
problems where current methods remain insufficient. The objective is to
demonstrate that the integration of ML and OR techniques can yield
robust improvements across different problem types.
About the group: The Optimization Group, led by Zdenek Hanzalek, focuses
on scheduling and combinatorial optimization. The group collaborates
strongly with high-tech companies (CEZ – Czech Energy Group, Porsche
Engineering Services, EATON, Skoda Auto, ST Microelectronics,
Volkswagen, DHL, …). Zdenek is the principal investigator of the
Roboprox project and organizer of SchedulingSeminar.com.
[1] Bengio, Y., Lodi, A. Prouvost, A. (2021). Machine Learning for
Combinatorial Optimization: a Methodological Tour d'Horizon, European
Journal of Operational Research, 290:405-421.
[2] Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev,
Reinforcement learning for combinatorial optimization: A survey,
Computers & Operations Research, Volume 134, 2021.
[3] Heinz, V.; Hanzálek, Z.; Vilím, P.: Reinforcement Learning for
Search Tree Size Minimization in Constraint Programming: New Results on
Scheduling Benchmarks, Computers & Industrial Engineering, Volume 209,
November 2025, 111413.
[4] Roman Václavík, Antonín Novák, Přemysl Šůcha, Zdeněk Hanzálek,
Accelerating the Branch-and-Price Algorithm Using Machine Learning,
European Journal of Operational Research, Volume 271, Issue 3, 2018,
Pages 1055-1069.
[5] Grus, J.; Hanzalek, Z.: Automated placement of analog integrated
circuits using priority-based constructive heuristic, Computers &
Operations Research, Volume 167, 106643, July 2024.
Contract details and dates
Hours Per Week: 40
Offer Starting Date: April 1, 2026
Application Deadline
Date and Time: March 31, 2026 - 23:45 (Europe/Prague)
Company/Institute:
Czech Institute of Informatics, Robotics and Cybernetics,
Czech Technical University in Prague,
City: Prague
Postal Code: 160 00
Street: Jugoslávských partyzánů 1580/3
Skills/Qualifications:
Motivation to perform excellent research, become part of the
world's research communities in your field, and publish in first-tier
scientific conferences and journals,
Ph.D. degree or equivalent (awarded or to be completed soon),
Co-author of at least 3 papers published in impact factor journals
or prestigious conferences,
Professional proficiency in spoken/written English (knowledge of
the Czech language is not required).
Specific Requirements: good background in scheduling, combinatorial
optimization and algorithm design/implementation.
Benefits:
An initial appointment for 1 year (with an extension of up to
2 years)
Salary around 70000 CZK gross monthly; check the Numbeo
database for the cost of living in Prague
Full social and health insurance
30 days of paid annual leave
Children's corner, kindergarten, and elementary school
operated by the Czech Technical University in Prague
Additional benefits such as subsidized meals, yearly benefits
supporting recreational and sports activities, as well as health care
programs
An informal and inclusive international working environment at
the Industrial Informatics Department, CIIRC, CTU in Prague.
Selection process:
Interested candidates are invited to submit their applications at:
https://forms.gle/hj7bMTuKM4ghzPC17 [using Postdoc Position ID:
04-Postdoc-Hanzalek]
The application package should contain:
Motivation letter (up to two pages), stating personal goals and
research interests
Academic curriculum vitae, including a list of publications
highlighting the three most important ones
Contact details for two to three referees who could support your
application,
A copy or a link to your Ph.D. thesis,
Date of your Ph.D. award or the expected date of your Ph.D. thesis
defense.
Euraxess code:
https://www.euraxess.cz/jobs/415421
--
Zdenek Hanzalek
Industrial Informatics Department,
Czech Institute of Informatics, Robotics and Cybernetics,
Czech Technical University in Prague,
Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
https://rtime.ciirc.cvut.cz/~hanzalek/
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