Post-Doc position available at LS2N, Nantes, France with mobility at NII Tokyo, Japan

When:
28/02/2022 – 01/03/2022 all-day
2022-02-28T01:00:00+01:00
2022-03-01T01:00:00+01:00

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LS2N, Nantes, France with mobility at NII Tokyo, J
Durée : 12 mois
Contact : Fabrice.Guillet@univ-nantes.fr
Date limite de publication : 2022-02-28

Contexte :
Title : Combining graph embedding and topic modelling for ontology/KG learning from large scale data

Key words : Topic modelling, Knowledge graph learning, Ontology learning, Graph embedding, Deep learning

Description. Ontology learning from the web data is a major challenging topic within the semantic web field and many approaches have been developed to tackle it. However, due to sparsity and heterogeneity of data, they lack to provide good quality results with a high semantical relevance for humans. The post-doc work aims to define a new approach for ontology learning/knowledge graph learning by incorporating embedded knowledge graphs in a clustering technique (topic modelling) dealing better with the sparsity and the heterogeneity of texts available in the Web and the semantical relevance of the results. The research domain of this post-doc position is model learning, linked data and graph embedding for ontology/knowledge graph learning from texts. Model learning/Topic modelling is one of the area expertise of the DUKe team (Data User Knowledge) of LS2N, one of the France’s leading public research labs in digital sciences. Linked data, graph building from texts and knowledge graph embedding are fields of expertise of the Japanese Ichise Laboratory from the National Institute of Informatics (NII), one of the leading research institute in Japan.

Duration : 12 months from (1 January 2022 -31 December 2022) including a mobility of 3 months in Japan .

Localization : Polytech Nantes, France , Ichise Laboratory, Tokyo Japan

Salary: 2900€ gross monthly + mobility expenses in Japan, during three months, about 350.000 yens / month.

Application: Candidates should have a PhD in computer science or applied mathematics, with strong experience in machine learning and related coding ecosystems in python. A background in semantic web and probability/statistics would be a plus.
Applicants should send a full CV including a complete list of publications and completed projects, a cover letter, and letters of recommendation or the names of two people who have worked with them.

Contact: Mounira Harzallah (Mounira.harzallah@univ-nantes.fr, Fabrice Guillet fabrice.guillet@univ-nantes.fr), DUKe, LS2N, France

Sujet :
Description. Ontology learning from the web data is a major challenging topic within the semantic web field and many approaches have been developed to tackle it. However, due to sparsity and heterogeneity of data, they lack to provide good quality results with a high semantical relevance for humans. The post-doc work aims to define a new approach for ontology learning/knowledge graph learning by incorporating embedded knowledge graphs in a clustering technique (topic modelling) dealing better with the sparsity and the heterogeneity of texts available in the Web and the semantical relevance of the results. The research domain of this post-doc position is model learning, linked data and graph embedding for ontology/knowledge graph learning from texts. Model learning/Topic modelling is one of the area expertise of the DUKe team (Data User Knowledge) of LS2N, one of the France’s leading public research labs in digital sciences. Linked data, graph building from texts and knowledge graph embedding are fields of expertise of the Japanese Ichise Laboratory from the National Institute of Informatics (NII), one of the leading research institute in Japan.

Duration : 12 months from (1 January 2022 -31 December 2022) including a mobility of 3 months in Japan .

Localization : Polytech Nantes, France , Ichise Laboratory, Tokyo Japan

Salary: 2900€ gross monthly + mobility expenses in Japan, during three months, about 350.000 yens / month.

Profil du candidat :
Application: Candidates should have a PhD in computer science or applied mathematics, with strong experience in machine learning and related coding ecosystems in python. A background in semantic web and probability/statistics would be a plus.
Applicants should send a full CV including a complete list of publications and completed projects, a cover letter, and letters of recommendation or the names of two people who have worked with them.

Contact: Mounira Harzallah (Mounira.harzallah@univ-nantes.fr, Fabrice Guillet fabrice.guillet@univ-nantes.fr), DUKe, LS2N, France

Formation et compétences requises :
see above

Adresse d’emploi :
see above