Today, many companies in various industries need to manage highly complex stochastic systems where uncertainties are inherent and data is ubiquitous. These systems span various domains, from manufacturing equipment, to rental systems, to production systems and beyond. In such environments, decisions must be made repeatedly to maximize profits while balancing conflicting outcomes and exploiting real-time data. For instance, in manufacturing operations, decisions regarding production speeds directly impact production outputs on the one hand, and factors such as equipment deterioration and energy consumption on the other hand. Similarly, in rental systems, optimizing pricing strategies to match real-time demand with supply is crucial for revenue maximization.

Given these complexities, there's a growing demand for advanced methodologies and algorithms aimed at devising revenue management strategies for complex stochastic systems. Leveraging the increasing availability of data and the seamless connectivity facilitated by the Internet of Things, these methodologies and algorithms capitalize on real-time data accumulation for instant decision-making. This PhD position presents an exciting opportunity to study revenue optimization problems for complex stochastic systems in various industries. This could involve everything from tractably modeling new revenue management problems to devising provably (near-)optimal algorithms to solve the established ones.

In this position you will work in the team of Zumbul Atan and be co-supervised by Collin Drent and Melvin Drent. Collin and Melvin, both assistant professors, specialize in stochastic decision-making under uncertainty, with applications in supply chain management, manufacturing, and maintenance. Zumbul Atan, an associate professor, focuses on revenue management and retail operations, with a particular emphasis on circularity.

• A master's degree (or an equivalent university degree) with excellent grades in Operations Research, (Applied) Mathematics, Industrial Engineering, or related field. A research master's degree (e.g., MRes, MPhil) is not required; however, graduating from a program with solid research training will be considered a plus.
• A strong mathematical background, including expertise in stochastic optimization (e.g. Markov decision theory and dynamic programming) and applied probability (Bayesian statistics).   
• Excellent coding skills (e.g., in Java, Python, Julia, MATLAB).
• A research-oriented attitude.
• Ability to be self-propelling and drive your own research.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Fluent in spoken and written English (C1 level).

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.