ORGANISATION/COMPANYUniversité Gustave Eiffel
RESEARCH FIELDComputer science
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE21/03/2022 17:00 - Europe/Brussels
LOCATIONFrance › Nantes
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/10/2022
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER101034248
Modern cities are facing important socio-economic and environmental challenges related to current transport systems. Noise and atmospheric pollutants generated by road traffic are important health hazards to the population. Traffic delays and various nuisances cause a loss of productivity with adverse effects on the economy. The assessment and reduction of disruptions related to transport systems is a strong societal demand, and represent a major concern for managers.
The urban system is complex and intricate, making transport system related decision-making difficult. This decision-making can be informed by models provided they can grasp the complexity of real world situations. The disaggregated representation of urban territories is a new paradigm for building relevant models. This representation is based on creating a precise spatio-temporal image of the urban territory. In order to have a reliable, precise and detailed representation, several steps are needed:
- the generation of a synthetic population representing the real population. The individuals of this synthetic population are spatially-detailed and specified by demographic, social and economic attributes that are in line with the assessments to be carried out;
- the assignment of activity plans to individuals including places of presence, activity types, schedules and travel;
- the assignment of the dynamic location of agents with the assignment of mode and routes taken during travel. This step requires using a multi-agent simulation model (Horni, 2016).
The representativeness of the generated synthetic population and the associated activity plans is essential for transport system assessments. The accuracy of the assessments strongly depends on the quality of the disaggregated data produced. The process of generating the disaggregated data is achieved using available data sources such as: small samples of detailed disaggregated data, aggregated data from the census, economic and social databases, mobile telephony data, etc.
The objective of the proposed PhD subject is to use machine learning techniques to generate a synthetic population and assign activity plans to individuals of this population. Innovative techniques such as deep generative models will be explored (Borysov, 2019). The application of these techniques will improve the quality and representativeness of the synthetic population and the associated activity plans. This in turn will greatly improve the results of simulations and evaluations.
Various heterogeneous data sources (preferably freely accessible) will be considered for the synthesis process. The ability to incorporate different data sources and the handling of incomplete data sources is expected to be an advantage of machine learning methods. The performance of the developed approach will be compared to traditional approaches, particularly those applied during previous studies in the host laboratories (Yameogo et al., 2021).
The methods developed will be generic and applicable to various urban contexts: medium-sized towns, large metropolises, in developed or developing countries. The PhD student will apply the new methods to two different urban areas (for example Nantes and Saigon).
The candidate will join a multidisciplinary and multicultural group of researchers and students working on different aspects of the evaluation of transport systems. The PhD student will provide methodologies for generating synthetic populations to researchers working on environmental and socio-economic assessments (social cost assessment, environmental justice, etc.). Those researchers will define the attributes of synthetic populations that are necessary for their assessments.
The candidate will apply innovative methods at the cutting edge of machine learning research in the artificial intelligence field. The methodologies developed will be applied as part of the research on major transport systems challenges. This thesis will be supervised jointly (Cotutelle) by Gustave Eiffel University and Vietnamese-German University. The PhD student will have the opportunity to develop close collaborations with research teams in France, Vietnam and internationally. In particular, he will spend 8 months to a year at the Vietnamese-German University.
Yameogo B.F., Vandanjon P.-O., Gastineau P., Hankach P. Generating a two-layered synthetic population for french municipalities: Results and evaluation of four synthetic reconstruction methods. Journal of Artificial Societies and Social Simulation, 24(2):5, 2021.
Yameogo B.F., Gastineau P., Hankach P., Vandanjon P.-O. Comparing methods for generating a two-layered synthetic population. Transportation research record, 2675(1), pp.136–147, 2021.
Borysov, S.S., Rich, J. and Pereira, F.C. How to generate micro-agents? A deep generative modeling approach to population synthesis. Transportation Research Part C: Emerging Technologies, 106, pp.73-97, 2019.
Horni, A., Nagel, K., and Axhausen, K. W. Chapter 1: Introducing MATSim. In The Multi-Agent Transport Simulation MATSim. London: Ubiquity Press. pp. 3-8, 2016."
This thesis will be in co-supervision (cotutelle) with Faculty of Engineering Vietnamese-German University (Vietnam). For more information, contact the PhD thesis supervisor.
- High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
- Access to cutting-edge infrastructures for research & innovation.
- Appointment for a period of 36 months based on a salary of 2 700 € (gross salary per month).
- Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year.
- International mobility will be mandatory
- An international environment supported by the adherence to the European Charter & Code.
- Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.
- At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree. At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
At the time of the deadline, applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately prior to the call deadline.
Applicants must be available to start the programme on schedule (around 1st October 2022).
- Please refer to the Guide for Applicants available on the CLEAR-Doc website.
- The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.
- Please contact the PhD supervisor for any additional detail on job offer.
- There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.
- Support service is available during every step of the application process by email: firstname.lastname@example.org
Web site for additional job details
REQUIRED LANGUAGESENGLISH: Good
- At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
- At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
- This thesis will be in co-supervision (cotutelle) with Faculty of Engineering Vietnamese-German University (Vietnam). For more information, contact the PhD thesis supervisor.
EURAXESS offer ID: 717326
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