In the past decades, mobility systems have been facing ever-increasing challenges in terms of users' dissatisfaction, road congestion and environmental pollution. In this context, the advent of vehicle connectivity and autonomy, powertrain electrification, and micromobility vehicles is providing us with unprecedented opportunities to address the aforementioned challenges. Specifically, these technologies enable centralized intermodal routing approaches that can potentially improve the travel experience (e.g., in terms of travel time and comfort), whilst reducing the costs and emissions incurred by operating the mobility system. However, to achieve this, some users must sacrifice part of their welfare for the sake of other users so that, overall, everyone is better off. This contrasts with the status quo, whereby each user behaves selfishly, and the system reaches a less efficient user-centric equilibrium.

Against this backdrop, this project will study fair incentive schemes to influence and steer human users towards the system optimum. With a similar scope, previous works have proposed monetary tolls. Yet money-based approaches are discriminating and inherently lack fairness. To overcome this limitation, this project will investigate the application of alternative approaches such as artificial currencies that cannot be bought nor exchanged but only paid or gained when travelling (see [1] for preliminary results), and other influential mechanisms. To cope with the complexity and uncertainty stemming from human behavior, the candidate will devise learning-based methods to adapt the mechanisms with the goal to steer the users—or at least 'nudge' them—towards the system optimum, whilst guaranteeing robustness and fairness with respect to diverse users' demand and preferences and environmental impact. A crucial part of the project will be the characterization of the societal costs defining the system optima, and of the different users' profiles and their interaction with the available modes of transportation. To account for this human-centric perspective in a mathematically tractable fashion, the candidate will leverage transdisciplinary collaborations within and outside of TU/e.

• Talented, creative, and enthusiastic candidates with excellent analytical and communication skills are encouraged to apply;
• a MSc degree (or equivalent) in Mechanical, Electrical Engineering, Systems & Control or a related discipline;
• a strong background in optimization and optimal control methods,
• experience and interest in transportation engineering, social science, game theory and learning are of benefit,
• fluent in spoken and written English.

• 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.