PhD offer - Complex graph data analysis with imperfect / incomplete data

PhD offer - Complex graph data analysis with imperfect / incomplete data

Start : As soon as possible

Contact all:

•    Baptiste Jeudy    baptiste.jeudy @
•    Charlotte Laclau    charlotte.laclau @
•    Christine Largeron    christine.largeron @ 

Title: Complex graph data analysis with imperfect / incomplete  data
Keywords: Data mining, Machine learning, Social network, Graphs analysis, Imperfect / incomplete data

General Overview:
In many applications, the data to be studied is relational, modeled in the form of a network represented by graphs. This representation allows capturing not only the information about entities (using attributes or properties) but also the relationships between them.
While the ability to discover knowledge from such network data is gaining in importance, the quality became a central issue in their exploitation. The aim of this PhD is first to study the impact of the lack of quality of relational data on the data mining and machine learning algorithms and, second to design robust methods to deal with imperfect and incomplete relational data and  able to provide explainable results

Working environment:
The PhD candidate will work at the Laboratoire Hubert Curien (UMR 5516) under the supervision of  Baptiste Jeudy, Charlotte Laclau and Christine Largeron, (Laboratoire Hubert Curien – Université Jean Monnet, Saint-Etienne, France).

The PhD fellowship is funded for 3 years from October 2022 and is monthly funded about approximatively 1500 €.

Profile of the candidate:
The candidate should have a master degree or equivalent in Computer Science. The subject is at the intersection of several domains: graph theory, statistics, data mining and machine learning. Thus the candidate should have strong backgrounds in several of these topics.
Other required skills:
•    Good abilities in algorithm design and programming.
•    Good technical skills regarding data mining, machine learning and data management
•    A very good level (written and oral) in English.
•    Good communication skills (oral and written).
•    Ability to work in a team with colleagues,
•    Autonomy and motivation for research.

Application instructions:
    Applicants are invited to contact us as soon as possible.
The application file should contain the following documents:
1.    a curriculum vitæ (CV);
2.    the official academic transcripts of all the candidate’s higher education degrees (BSc, License, MSc, Master’s degree, Engineer degree, etc.). If the candidate is currently finishing a Master’s degree, s/he must send the transcript of the grades obtained so far, with the rank among her/his peers, and the list of classes taken during the last year;
3.    some recommendation letters (quality is more important than quantity, there);
4.    and a motivation letter written specifically for this position.

Send all of these documents by email to all the advisors:

•    Baptiste Jeudy    baptiste.jeudy @
•    Charlotte Laclau    charlotte.laclau @
•    Christine Largeron    christine.largeron @ 

Interviews will be conducted as they arise and the position will be filled as soon as possible

Important note:
This internship is part of an international collaboration between the Alberta Machine Intelligence Institute (AMII) at the University of Alberta in Edmonton and the Laboratoire Hubert Curien, notably within the framework of the IEA CODANA supported by the CNRS.  As part of the thesis, the doctoral student could stay at the AMII at the University of Alberta in Edmonton (Canada).