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Annonce

20 mai 2022

Thèse : Deep learning approaches and spatial relations reasoning for interpreting Byzantine seals


Catégorie : Doctorant


Supervisors:
– Isabelle BLOCH, LIP6 Laboratory, Sorbonne Université
– Victoria EYHARABIDE, STIH Laboratory, Sorbonne Université
Location: LIP6 UMR7606 - Laboratoire de recherche en informatique, Sorbonne Université.

Ecole Doctorale: ED 130 - EDITE https://www.edite-de-paris.fr/
Start date: Octobre 2022
Keywords: Deep learning. Instance segmentation. Fuzzy Logic. Knowledge and model of reasoning. Byzantine sigillography
 

Deep learning approaches and spatial relations reasoning for interpreting Byzantine seals

Supervisors:

– Isabelle BLOCH, LIP6 Laboratory, Sorbonne Université
– Victoria EYHARABIDE, STIH Laboratory, Sorbonne Université
Location: LIP6 UMR7606 - Laboratoire de recherche en informatique, Sorbonne Université.

Ecole Doctorale: ED 130 - EDITE https://www.edite-de-paris.fr/
Start date: Octobre 2022
Keywords: Deep learning. Instance segmentation. Fuzzy Logic. Knowledge and model of reasoning. Byzantine sigillography

Abstract

The general aim of the thesis is to combine computer vision, knowledge engineering, and mathematical modeling of spatial relationships to help with the interpretation of Byzantine seals. This research aims to (i) work on the recognition of objects on seals to analyze iconographic scenes; (ii) estimate the inception date of Byzantine seals; and (iii) propose solutions based on hybrid AI techniques to interpret damaged areas based on existing insights. The objective is to explore new artificial intelligence methods applied to Byzantine sigillography: instance segmentation with deep learning, knowledge graph embeddings, and spatial reasoning for image understanding. The thesis is expected to contribute to the intersection of computer vision, knowledge representation, and spatial logic.

Profile of applicant

The candidate must fit the following requirements:
• A Master degree in Computer Science or Engineering is required.
• Advanced skills in Python programming are mandatory.
µ• A strong background in Machine Learning, Deep Learning, and image analysis using related libraries (scikit-learn, Tensorflow, Pytorch, etc.).
• Motivation for interdisciplinary research will be appreciated, as well as for artificial intelligence at large.
• Fluency in written and spoken English is essential.
• Communication skills in French are required too.

Research environment

The proposed thesis will be carried out within the LFI (Learning, Fuzzy and Intelligent systems) team at LIP6. Isabelle Bloch and Victoria Eyharabide will jointly supervise the PhD candidate to work on new approaches combining knowledge graph embeddings [7] and mathematical modeling of spatial relationships [5] for image understanding. This research will be developed within the framework of the ANR BHAI project, grant number ANR-21-CE38-0001 https://anr.fr/Project-ANR-21-CE38-0001, started in October 2021 for 4 years.

Application

Applicants should send an email to Isabelle Bloch isabelle.bloch@sorbonne-universite.fr and Victoria Eyharabide maria-victoria.eyharabide@sorbonne-universite.fr with:
• A full curriculum vitae, including a summary of previous research experience.
• A transcript of higher education records
• A one-page research statement discussing how the candidate’s background fits the proposed topic
• Two support letters of persons that have worked with them.

 

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