Are you interested in adapting techniques from Artificial Intelligence (specifically Deep Reinforcement Learning (DRL)) to support people who plan transportation and logistics operations in practice? Do you want to help develop cutting-edge techniques, i.e. DRL algorithms inspired by the latest breakthoughs in the field and/or hyperparameter tuning and algorithm selection for DRL using AutoML? Are you motivated to make these techniques applicable for our partners from practice, including ASML and Vanderlande? We are looking for two PhD students in Operations Research and Artificial Intelligence with a focus on those topics.

Current breakthroughs in Artificial Intelligence are exciting for games like Go and Chess, where it is crucial to anticipate unknown moves of the opponent. When making logistics decisions, it is equally important to anticipate the arrival of new data (e.g., orders, delays, and disruptions).
For many such problems, Deep Reinforcement Learning algorithms like AlphaZero have been demonstrated to be game-changers. The logistics sector recognizes the opportunities and is eager to adopt deep reinforcement learning. However, companies struggle to translate the abstract possibilities of deep reinforcement learning into solutions for their own logistics problems.

To address this challenge, we aim to develop a toolbox that contains the tools to rapidly model and solve logistics problems with Deep Reinforcement Learning, preferably using zero-code solutions. The toolbox will be tested by using it to solve concrete logistics problems at one or more of our ten project partners.

You, as a successful applicant, will perform research on the project outlined above in an international research team. You will discuss possible your ideas with partners from practice.
You will report research findings at international conferences and workshops, and in high-quality scientific journals.  The research will be concluded with a PhD thesis. A small teaching load (on average about 10%) is part of the job description.

We are looking for two PhD students, one will be oriented more towards integrating Deep Reinforcement Learning techniques into an easy-to-use toolkit. This position will be based in
the Operations Planning Accounting and Control (OPAC) Group and will be supervised by Willem van Jaarsveld and Remco Dijkman. The other position will be oriented more towards the development of the Deep Reinforcement Learning techniques themselves. This position will be based in the Information Systems (IS) Group and will be supervised by Yingqian Zhang and Willem van Jaarsveld.

Both positions will be based in the DynaPlex project and require strong collaboration with each other, with another PhD position at the University of Twente, and with our partners from practice, including ASML and Vanderlande.

• You have a Master's degree in Industrial Engineering, Operations Research, Computer Science, Data Science, Artificial Intelligence, or a similar field of study.
• You have a strong programming background, preferably in C or Python.
• You have a strong affinity with interdisciplinary research and enjoy collaborating with others.
• You can work on a challenging topic that has both fundamental and applied research aspects.
• Your communication skills are excellent as is your proficiency in English and your ability 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 a target start date in September 2021 and 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.