In the past years, Deep Reinforcement Learning methods integrated with planning methods have been very successful in solving complex Sequential Decision Problems, e.g., in games such as Go. These methods can handle very large state spaces, however, less so large action spaces.

The proposed PhD research project aims to develop new hybrid solution approaches for (stochastic) Sequential Decision Problems with discrete, high-dimensional, and linearly constrained decision spaces as often encountered in Operations Research. To this end, the PhD student will integrate (Deep) Reinforcement Learning methods into traditional Operations Research methods, such as Rolling Horizon approaches using Stochastic Programming. In this way, at the time of decision-making, we combine planning by looking ahead and learning from previous experience. We aim to provide theoretical and empirical results, showing the superiority of the new methods compared to the state of the art in terms of computation at the time of decision-making and the quality of the solution. We will transition from problems with known dynamics to (partly) unknown dynamics. Therefore, we will explore solution strategies that transition from robust optimization to distributionally robust optimization and finally to stochastic optimization.

The PhD student will implement the algorithms and apply them to real-world use cases in Healthcare Logistics, such as multi-appointment scheduling, surgery scheduling, and resource allocation in times of scarce healthcare capacity. Therefore, the PhD student will also be part of the inter-faculty group CHOIR (Centre for Healthcare Operations Improvement and Research). CHOIR is a research center within the UT, and it is currently one of the most active and productive research groups in the field of Operations Research and Management in Healthcare. Through Research, Education, and Valorization, we help healthcare practitioners face their complex logistical challenges.

• You have acquired a Master's degree in mathematics, operations research, econometrics, industrial engineering, or a closely related discipline.
• You are an enthusiastic and highly motivated researcher.
• You have a creative mindset and excellent analytical and communication skills.
• You have a good team spirit and like to work in an interdisciplinary and internationally oriented environment.
• You are proficient in English.
• The UT and the faculty EEMCS are inclusive toward underrepresented groups and strive to increase the proportion of female staff. Female applicants are particularly welcome.

• As a PhD candidate at UT, you will be appointed to a full-time position for four years, with a qualifier in the first year, within a very stimulating and exciting scientific environment;
• The University offers a dynamic ecosystem with enthusiastic colleagues;
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU);
• You will receive a gross monthly salary ranging from € 2.770,- (first year) to € 3.539,- (fourth year);
• There are excellent benefits, including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• The flexibility to work (partially) from home;
• A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours. A full-time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis.
• Free access to sports facilities on campus
• A family-friendly institution that offers parental leave (both paid and unpaid);
• You will have a training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• We encourage a high degree of responsibility and independence, while collaborating with close colleagues, researchers and other staff.

The position will be in the Applied Mathematics department. The Applied Mathematics department has an active research portfolio in stochastic operations research, algorithmic discrete mathematics, complex networks, statistics, systems theory, computational science, and artificial intelligence with applications in health care, energy systems, traffic, and imaging. See MOR and SACS, and MDS for information.

Our research group, Stochastic Operations Research (SOR), conducts mathematical education and research of internationally high standards in the areas of stochastic processes and mathematics of operations research to contribute to the development of mathematics in a multidisciplinary engineering environment and contribute to a better understanding and functioning of our increasingly complex society. See SOR.