PhD Position in Shared Mobility Systems: Optimization, Pricing and Data-Driven Methods

Université Gustave Eiffel - Site de Marne-la-Vallée; Champs-sur-Marne; On-site; EUR 21,000 - 27,000; Full time

Job summary

Université Gustave Eiffel - Site de Marne-la-Vallée invites applications for a 36-month PhD position in Shared Mobility Systems focused on optimization, pricing and data-driven methods. The successful candidate will join the GRETTIA research team, contributing to advanced quantitative modelling and simulation within a cross-disciplinary setting.

The project, part of the ANR JCJC FlexiShare, aims to develop equitable and sustainable mobility solutions using machine learning, reinforcement

Qualifications

• Strong quantitative background in transport or related field.
• Master’s degree or equivalent in a relevant domain.
• Interest in shared mobility, optimization, and data-driven methods.

Responsibilities

• Develop and apply optimization, pricing, and data-driven methods for shared mobility systems.
• Contribute to modelling, simulation, and evaluation of mobility scenarios.
• Publish results and collaborate with the GRETTIA team across disciplines.

Skills

Mathematical modelling

Optimization

Programming

Data analysis

Machine learning

Education

Master’s degree in Transportation Engineering/Operations Research/Data Science

Tools

Java

MATSim

Python

Job description

PhD Position in Shared Mobility Systems: Optimization, Pricing and Data-Driven Methods

05/10/2026 Contrat doctoral

PhD Position in Shared Mobility Systems: Optimization, Pricing and Data-Driven Methods

Shared mobility, transportation systems, operations research, optimization, pricing, incentives, machine learning, reinforcement learning, simulation, transport economics

Within the ANR JCJC project FlexiShare (Equitable Shared Mobility for Sustainable Cities), GRETTIA at Université Gustave Eiffel is recruiting a PhD student for a 36-month doctoral position, with a planned start around January 2027, with some flexibility regarding the exact starting date.

The research will contribute to the development of advanced methods for the analysis, design, and improvement of shared mobility systems, with particular attention to quantitative modeling, optimization, simulation, pricing, and data-driven approaches.

The PhD student will join the GRETTIA research team at Université Gustave Eiffel and work in a collaborative and interdisciplinary environment at the intersection of transportation, operations research, simulation, artificial intelligence, and mobility analysis.

The Gustave Eiffel University is a national multi-campus university. It was created on 1 January 2020 as a result of the merger of the University of Paris-Est Marne la Vallée, Ifsttar, and several schools and engineering schools: EIVP, ENSG, ESIEE, and Ecole d'architecture Paris Est. Gustave Eiffel University has more than 2,500 agents and 17,000 students at nine sites in France. The university hosts a quarter of the national research and development on sustainable cities and is home to the largest transport research centre in Europe.It is a university on a human scale, bringing together multi-disciplinary skills that enable it to conduct quality research for the benefit of society, to offer training courses adapted to the socio-economic world, and to support changes in society and public policies.

The GRETTIA research lab is part of Université Gustave Eiffel and carries out its research activity in the field of land transport systems. It is interested in all aspects of road and guided transport modes, from systemic aspects, modelling and simulation to the dynamic aspects of vehicles, including management, diagnosis and maintenance. GRETTIA contributes to the development of transport network and systems engineering, taking into consideration the issues of integration, intermodality, reliability and system analysis. Within this framework, the research unit conducts a transversal activity of development of models and generic tools based on the field of mathematical engineering, advanced computer science and mechanics; contributes to the modelling, design, management,evaluation and maintenance of intelligent transport and infrastructure operation systems; studies the conditions of functional and social acceptability of new transport services.

We are looking for a candidate with a strong quantitative background and a Master’s degree in Transportation Engineering, Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, Data Science, Artificial Intelligence, Transport Economics, or a closely related field.

A strong candidate would ideally have several of the following skills:

• solid foundations in mathematical modelling and optimisation;
• knowledge of stochastic and robust optimisation;
• knowledge of machine learning and reinforcement learning;
• good programming and numerical analysis skills;
• an interest in transportation systems, shared mobility, simulation, pricing, incentives, and user behaviour;
• experience with transportation modelling, simulation-based optimisation, Java, MATSim, transport economics, behavioural modelling, or data-driven transportation methods would be an advantage.