Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : Laboratoire Bordelais de Recherche en Informatique
Durée : 24 months
Contact : meghyn.bienvenu@labri.fr
Date limite de publication : 2021-06-15
Contexte :
Accessing the relevant information contained in real-world data to support informed decision making is difficult, time-consuming, and error-prone due to the need to integrate data across multiple heterogeneous sources. Moreover, even if this first hurdle is overcome, a perhaps even more daunting challenge arises: how to obtain reliable insights from imperfect data? It is widely acknowledged that real-world data is plagued with quality issues, such as incompleteness (missing information) and errors (false or outdated information).
The ontology-based data access (OBDA) paradigm addresses the first challenge by facilitating access to (potentially heterogeneous) data sources through the use of ontologies that specify a convenient user-friendly vocabulary for query formulation (which abstracts from the way the data is stored) and capture domain knowledge that can be exploited at query time, via automated reasoning, to obtain more complete query results. While OBDA systems are growing in maturity, they too often fail to address the data quality issue, aside from issuing warnings when inconsistencies are discovered.
The postdoctoral researcher will take part in the INTENDED Chair on Artificial Intelligence (https://intended.labri.fr/), whose aim is to develop intelligent methods for handling imperfect data.
Sujet :
The postdoc will be involved in developing principled and effective methods for handling imperfect data. The precise topic will depend on the interests and background of the selected researcher, so brilliant candidates with an interest in the overall project topics should not hesitate to apply.
As an example research direction, one of the main aims of the project is to develop pragmatic approaches to handling inconsistencies in more expressive settings. This could include conceiving new inconsistency-tolerant query answering algorithms for more expressive ontology languages, devising optimizations, identifying interesting tractable subcases, and extending algorithms to tackle other important features (e.g. mappings, uncertain information, temporal dimension) that have been little explored thus far.
The position is primarily concerned with foundational research, but depending on the interests and aptitude of the postdoctoral researcher, it could also involve a more practical component with the implementation and testing of the developed algorithms.
Profil du candidat :
Applicants should possess an excellent research record, as demonstrated by publications in top venues, with prior experience in databases or knowledge representation and reasoning.
Familiarity with first-order logic and/or ontology languages is desirable.
Experience in one or more of the following areas would be relevant and welcome: data integration, data cleaning, data quality, description logics, ontologies, automated reasoning, inconsistency handling, theoretical computer science (esp. computational complexity).
Strong English language skills (reading, writing, & speaking) are expected, but knowledge of French is not required. The working language can be either French or English.
Formation et compétences requises :
At the time of hiring, applicants must hold a PhD in computer science (or possibly a related discipline with significant computer science experience).
Students near completion of their PhD degree may also get in touch, as there is flexibility on the starting date.
Adresse d’emploi :
LaBRI – Université de Bordeaux
351 cours de la Libération
F-33405 Talence cedex
Document attaché : 202105051420_postdoc1-intended.pdf