1. You recently obtained your master’s degree (or you are about to graduate in 2026) in Business Engineering, Econometrics, Statistics, Mathematics, Actuarial Sciences, Computer Science, Civil Engineering, or Industrial Engineering or a similar degree with an equivalent academic level.
2. You have a strong quantitative background and an interest in data-driven research. Prior knowledge of causal machine learning, econometrics, or related machine learning methods is considered an asset, but motivation to develop expertise in these areas is essential.
3. Excellent results in prior studies are required (cum laude — distinction, or higher).
4. Experience with programming languages (such as Python, MATLAB, or R) is an asset.
5. You can conduct independent research (demonstrated, e.g., by an excellent Master’s thesis) as well as collaborate well in teams.
6. You have excellent command of spoken and written English and are equipped with good communication skills.
7. You are willing to participate in (international) seminars and conferences.
8. You are willing to enrol in the PhD program of the Faculty of Economics and Business at KU Leuven, which entails 21 ECTS of relevant coursework.
9. You will also give guidance to students, be involved in teaching activities and in the supervision of exams.
This PhD project investigates the pricing of full-service maintenance contracts, which cover all preventive and corrective maintenance activities over a predefined contract horizon for a fixed, upfront agreed price. Such contracts play a central role in industrial service operations, yet their pricing remains complex due to uncertainty in future maintenance demand and heterogeneity in customer preferences.
Existing pricing approaches typically follow either a cost-plus strategy, based on predictions of future maintenance costs, or a value-based strategy, reflecting customers’ willingness to pay. While both approaches are well established, they do not explicitly quantify the causal effect of pricing decisions on customer acceptance.
The core objective of this PhD project is to develop and apply causal machine learning methods to estimate the probability that a customer accepts a given price offer. Causal machine learning integrates ideas from econometrics and machine learning to estimate treatment effects while controlling for confounding factors. In contrast to purely predictive models, these methods allow researchers and practitioners to reason about counterfactual outcomes, such as how acceptance probabilities would change under alternative pricing strategies, making them particularly suitable for pricing and operational decision-making problems.
Empirically, the project will use a rich dataset consisting of historical price quotations combined with detailed information on customers, machines, contract characteristics, and other relevant contextual features. Based on these predictors, the PhD candidate will develop causal models to estimate acceptance probabilities for alternative price offers and to generate insights into the drivers of customer decision-making in service contracts.
The project is situated in the field of Operations Management, with strong links to service operations, pricing, and data-driven decision support. The PhD candidate will gain advanced methodological training in causal inference and machine learning, while working on a problem with clear managerial relevance.
The Research Centre for Operations Management, Faculty of Economics and Business, KU Leuven (Belgium) has a vacancy for a PhD student position at the intersection of Operations Management and Data Analytics. We are looking for a full-time PhD student to do research on causal machine learning for maintenance service contracts, under the supervision of profs. Robert Boute, Wouter Verbeke, and Katrien Antonio.
10. A fulltime PhD position for 1 year, starting 2026, September 15, renewable up to a maximum of four years upon yearly successful evaluations. The work should lead to a PhD degree.
11. An interesting and diverse job within a dynamic and pleasant working environment, in a group conducting research at the highest international standards.
12. A social, tolerant, and constructive workplace that offers ample opportunities for personal development.
13. A correct and beneficial salary.
14. Numerous opportunities for personal growth, illustrated by e.g., conference participation, “doctoral schools” courses on transferable skills, participation in educational initiatives, and publication opportunities.
Applications should be submitted through KU Leuven’s online platform together with:
15. a motivation letter with a statement of skills and research interests;
16. a curriculum vitae with at least 2 references;
17. a writing sample from the master thesis or relevant (individual) coursework;
18. university diploma copies and complete transcripts.