Causal Machine Learning-Based Decision Support for Railway Control Rooms: Postdoctoral position

Do you have experience in Machine learning?, Do you have a Master's degree?, Candidates should have a strong track record in event-level big data management, observational data analysis, and the development, coding, and application of forest- or tree-based instrumental-variable causal machine learning methods. Expertise in empirically oriented behavioral operations management is also expected. Familiarity with railway control-room settings, causal inference on train punctuality, employee workload research, or human-algorithm interaction would be a strong asset.

The selected candidate is expected to be fluent in English, hold a PhD in a topic related to the aforementioned fields, and have a proven research track record.

Professional experience and knowledge of French are a plus, but not a requirement.

Finally, we are looking for candidates who embody our core values of Accomplishment, Responsibility, Integrity, Solidarity, and Engagement, and fit our diverse and international culture in which 50 nationalities are represented. IÉSEG places tremendous value on diversity and inclusion, and we therefore encourage applications from all backgrounds. Underrepresented groups in academia (notably women, minority ethnic groups, people identifying as LGBTQIA+, people with a (non-)visible disability, and people from lower socio-economic backgrounds and/or economically developing countries) are particularly encouraged to apply, as we are highly committed to ensuring that IÉSEG reflects and benefits from the diversity present in society.

The selected applicant is expected to begin his/her/their assignment in October 2026 at the earliest. The appointment is for 12 months (with the possibility of renewal). Gross salary is competitive with other European research and academic institutions. The researcher will be based on the campus of IÉSEG School of Management in Lille.

At IÉSEG, we are committed to providing a supportive and engaging work environment. To new faculty members (and their partner), we offer a wide range of resources to help them integrate into the country, culture and school, such as individualized French language courses, a relocation allowance, and housing search assistance. Moreover, all faculty members have access to a competitive salary, as well as benefits such as French social security, a complementary health insurance, meal vouchers, a profit-sharing plan, and a contributory pension scheme. Finally, the school actively promotes research, provides resources for active scholars (e.g., funding for conferences and data collection) and offers competitive financial bonuses for high quality international peer-reviewed research publications.

IÉSEG holds the "triple crown" of international accreditations (AACSB, AMBA & EQUIS) and is a member of the "Conférence des Grandes Écoles". The School offers Bachelor, Master and Post-Graduate Degrees as well as Executive Education programs. IÉSEG is also one of the leading French business schools in terms of research. The IÉSEG Research Center is accredited by the French CNRS (National Center for Scientific Research). Our Lille Campus is in the heart of Northern France (within the triangle made up by London, Paris and Brussels), and our Paris Campus is located in the biggest European business district of "La Défense". More information on the school can be found here: www.ieseg.fr/en, Research will be the main focus of the selected candidate as he/she/they will contribute to the vibrant research community at IESEG through their commitment to research excellence. The position focuses on the development and application of causal machine learning-based decision support tools for railway control rooms, with particular emphasis on employee workload, human-machine interaction, and the mitigation of railway delay propagation. The position is part of the ANR-funded BALANCE project, Équilibrer la charge de travail pour améliorer le bien-être des employés dans un contexte digital, and the externally funded DOMINO project, Mitigating Delay Propagation in Railway Networks: Optimal Control Room Strategies. The selected candidate will work with available company data related to knowledge workers in digital control rooms and will be expected to ensure the practical relevance and validity of the findings for the partner organization. The candidate will receive regular mentorship and will contribute to the dissemination of research through academic manuscripts targeting leading management, operations management, and operations research journals. The selected candidate is expected to conduct research and write papers in collaboration with the other team members. In addition, the position offers opportunities to interact with an international network of scholars specializing in operations research, operations management, behavioral operations, and data-driven decision support.