We are looking for a PhD candidate with a background in operations research, mathematics, econometrics, industrial engineering, or computer science. Especially candidates with the interest to work on the interface of optimization and AI are highly encouraged to apply. The project is centered around the development of new optimization methods (using machine learning, operations research, AI, deep reinforcement learning) needed for solving challenging operations management problems in, for example, retail operations, transportation, and logistics.

Project Description
Organizations frequently make decisions while facing an uncertain future. For example, inner-city stores are replenished by trucks before sales are known, and ICU beds are reserved each day before COVID-19 patient inflow is known. Making good decisions is essential for organizations and society. For example, congestion and pollution from trucks in inner-cities are reduced, and COVID-19 and regular patients receive better healthcare.

In this project, we focus such problems that require both a structural decision and daily dynamic decisions. The structural decision, for instance the reservation of ICU capacity for the coming days or weeks, has to consider the structure of the daily dynamic decisions, for instance the allocation of patients between hospitals.

Traditionally, (mixed) integer optimization is the method of choice for determining structural decisions, and when taking uncertainty into account methods are typically grounded in Stochastic Programming or Robust optimization. Alternatively, Markov decision processes and (nowadays) deep reinforcement learning/AI are the relevant fields for determining dynamic policies. How to combine both approaches is an exciting new research field in which this project will make fundamental and applied contributions.

The project will revolve around combining methods from both fields, developing new and novel solution approaches, and applying them on practical use-cases from retail operations and/or transportation. The project is envisioned to take place in three (related) steps:

1. Study the theory of joint structural and dynamic decision-making.
2. Using machine learning methods to learn a smart and compact representation of the dynamic decision problem that can be included in the structural decision problem.
3. Development of advanced methods tailored towards specific use-cases, for instance in the replenishment of retail stores or balancing capacity/containers in (transport) networks. With many connections to industry and business this will be aligned as fit with the project progress.

You, as a successful applicant, will perform the PhD project outlined above. The research will be concluded with a PhD thesis. You will be supervised by dr. Albert H. Schrotenboer. A small teaching load is part of the job.

Academic and Research Environment
You will be part of the Operations Planning, Accounting & Control group (OPAC). OPAC currently consists of 25 staff members, 10 postdocs and 45 PhD students. The faculty teaches and conducts research in the area of operations planning and control in manufacturing, maintenance services, logistics and supply chains. Research is generally quantitative in nature, while many of the researchers also engage in empirical research. The OPAC group is responsible within the university for all teaching in the areas of operations management, transportation, manufacturing operations, reliability and maintenance, and accounting and finance, both at undergraduate and graduate level. The OPAC group has close collaborations with the industry, which gives direct access to challenging operations management problems, new technologies, and empirical data.

• A Master's degree in Operations Research, Econometrics, Industrial Engineering, Operations Management, Applied Mathematics, Computer Science, or a related field.
• Strong analytical and mathematical skills and demonstrated competence for quantitative modelling.
• Basic knowledge of machine learning concepts, or a sound statistical background.
• Workable knowledge of a programming language like C/C++ or Java, or the willingness to learn this in the first half year. Semi advanced knowledge of other programming languages such as Python suffices too.
• Experience with mathematical programming solvers and/or machine learning libraries is appreciated.
• Knowledge and experience with Markov decision process theory and (Mixed) Integer Optimization theory is highly appreciated.
• Affinity with the area of logistics and supply chain management.
• Proven excellent verbal and written communication skills.
• High proficiency in English and being able to collaborate in an international setting.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.