Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : LS2N
Durée : 36 mois
Contact : claudia.marinica@univ-nantes.fr
Date limite de publication : 2024-03-31
Contexte :
One fully funded PhD position (3 years) is available on the topic of Analyzing Semantic Indoor Trajectories for understanding Museum visitors’ movement at the Laboratory of Digital Sciences of Nantes (LS2N) in Nantes University, France.
This PhD subject is built on an international collaboration between DUKe research team of LS2N lab, Nantes, France, and the Museology Research Laboratory of the Department of Archives, Library Science and Museology at the Ionian University in Corfu, Greece, together with museums in Corfu, Greece, including the Corfu Museum of Asian Art, the Mon Repos Museum in Corfu, and the Corfu art Gallery.
Sujet :
Co-supervisors:
Assistant Professor Claudia Marinica, Assistant Professor Fabien Picarougne, Full Professor Fabrice Guillet from LS2N/Nantes University, France
Context and scope:
Museums have been studying their visitors for decades to understand why visitors go to museums, what they do there, how they learn, and what their engagement and satisfaction may be. The main objective of this PhD subject is to develop new techniques for visitors’ movement analysis, by building for each visitor its trajectory inside the museum. The originality of this work comes from enhancing visitors’ trajectories with (1) indoor space constraints restraining the visitor’s movement (e.g. position of doors, corridors, etc.), and (2) contextual and/or semantic information related to the museum or the visitor. Thus, in this PhD, we propose to work towards 3 challenges: (1) express new movement collected data under an existing formalism, called SITM (Semantic Indoor Trajectory Model), (2) develop trajectory data mining techniques applied over SITM trajectory data to extract trajectory patterns describing the visitors’ movement, (3) while the previous challenge aims to help museums to enhance visitors’ experience, this third challenge aims to encourage the museums to take managerial decisions (such as deriving improved evacuation routes) by providing movement predictions. To this end, we propose to formalize SITM trajectories as trajectory time series and to work towards developing trajectory time series classification algorithms. Visitors’ data already collected from partner Museums and to be collected through LBS or systems comprise datasets to be analyzed.
Detailed description of the subject is available here:
https://uncloud.univ-nantes.fr/index.php/s/SZcteLRe7TDZY4N
*****
Expected start: September/October, 2024
Application deadline: April 1st, 2024
We offer. The PhD will take place in the DUKe research team of LS2N lab, Nantes, France. The most of the DUKe team members work in the Polytech Nantes Engineering School of the Nantes University, thus the PhD Student will have an office there. We are a dynamic research team composed of 20 permanent researchers and around 10 PhD students and postdocs.
Given the context of the international collaboration of the PhD subject, the PhD student will maybe have to make some research stays in the Museology Research Laboratory at the Ionian University in Corfu, Greece and also visit some of the Museums providing the data.
How to apply. Interested candidates can submit their applications by sending:
– Curriculum vitae
– Letter of Motivation specific to this PhD position
– Abstract of master thesis
– At least two recommendation letters
– Degree certificates for the recent years
– List of publications (if any)
Contact: Assistant Professor Claudia Marinica (Claudia.Marinica@univ-nantes.fr)
Profil du candidat :
Your profile. We expect to welcome a candidate fulfilling the following requirements:
– Completion of an excellent master or diploma in Computer Science
– Strong programming skills and experience
– Background knowledge in the following areas are highly appreciated: data mining, deep learning, time series classification
– Ability to develop methods and concept
– Willingness to contribute in interdisciplinary projects
– Organizational and analytical skills
– Ability to work in a team, problem-solving skills, and creative thinking
– Excellent spoken and written communication skills in English
Formation et compétences requises :
Your profile. We expect to welcome a candidate fulfilling the following requirements:
– Completion of an excellent master or diploma in Computer Science
– Strong programming skills and experience
– Background knowledge in the following areas are highly appreciated: data mining, deep learning, time series classification
– Ability to develop methods and concept
– Willingness to contribute in interdisciplinary projects
– Organizational and analytical skills
– Ability to work in a team, problem-solving skills, and creative thinking
– Excellent spoken and written communication skills in English
Adresse d’emploi :
Polytech Nantes, Rue Christian Pauc, 44300 Nantes
Document attaché : 202403010840_A-SITM PhD subject.pdf