Tenure-Track Scholar in Data-Driven Optimization & Learning

UCLouvain; Louvain-la-Neuve; Remote; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Tenure after probation

Progressive salary

Work-life balance

Job summary

UCLouvain seeks to recruit a full-time academic position in Optimization and Learning for Data-Driven Decision systems within the Louvain School of Management (LSM) and the Louvain Institute of Data Analysis and Modeling (LIDAM/CORE). The role combines teaching, research, and service duties across multiple units and campuses, with a start date in 2027.

The candidate will develop high-level skills in coding, optimization, and learning-based decision methods, contribute to a strong research

Qualifications

• PhD in Operations Research or related field.
• Proven teaching experience and strong independent research record.
• Experience securing research funding and supervising doctoral students is a plus.

Responsibilities

• Develop a strong, internationally visible research agenda in mathematical optimization and data-driven decision systems.
• Teach courses in programming, optimization, AI, and quantitative decision making in English and French at bachelor and master levels.
• Supervise doctoral students and collaborate with CORE, LIDAM, and ICTEAM, contributing to service activities within the institution.

Skills

Mathematical optimization

Operations research

Machine learning

Data-driven decision making

Predict-then-optimize

Reinforcement learning

Interdisciplinary collaboration

Education

PhD in a quantitative field

Job description

UCLouvain seeks to recruit a full-time academic position in Optimization and Learning for Data-Driven Decision systems within the Louvain School of Management (LSM) and the Louvain Institute of Data Analysis and Modeling (LIDAM/CORE). The role combines teaching, research, and service duties across multiple units and campuses, with a start date in 2027.

The candidate will develop high-level skills in coding, optimization, and learning-based decision methods, contribute to a strong research