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

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

Université Gustave Eiffel - Site de Marne-la-Vallée

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

Expertises

JavaRMachine LearningIA

il y a 14 jours

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Information importante


Localisation :

77420 Champs-sur-Marne, France

Mode de travail :

Sur site

Publié le :

5 octobre 2026

Le besoin


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

Réf ABG-140407

Sujet de Thèse

05/10/2026

Contrat doctoral

Université Gustave Eiffel - Site de Marne-la-Vallée

Lieu de travail

Champs sur marne - Ile-de-France - France

Intitulé du sujet

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

Champs scientifiques

  • Sciences de l’ingénieur

Mots clés

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

Description du sujet

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.

Nature du financement

Contrat doctoral

Précisions sur le financement

Présentation établissement et labo d'accueil

Université Gustave Eiffel - Site de Marne-la-Vallée

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 center 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, modeling and simulation to the dynamic aspects of vehicles, including management, diagnosis and maintenance. GRETTIA contributes to the development of transport network and system 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 modeling, design, management,evaluation and maintenance of intelligent transport and infrastructure operation systems; studies the conditions of functional and social acceptability of new transport services.

Profil du candidat

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 modeling and optimization;

  • knowledge of stochastic and robust optimization;

  • 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 behavior;

  • experience with transportation modeling, simulation-based optimization, Java, MATSim, transport economics, behavioral modeling, or data-driven transportation methods would be an advantage.

Date limite de candidature

15/11/2026

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