PhD student in Artificial Intelligence M/F CESI Nanterre CDD

The post

Title: Towards an Explicable and Equitable Artificial Intelligence (XAI)

Scientific fields: Artificial intelligence, data science, machine learning, statistics.
Keywords: XIA or explainable AI methods, fairness algorithms, statistical learning, deep neural network, predictive modeling, biases, decision theory.

Thesis supervisor:
Ndeye Niang Keita, Head of the MSDMA CEDRIC CNAM team.

Co-director or co-supervisor(s):
Genane YOUNESS, Teacher-Researcher, LINEACT CESI.

Research work
Abstract thesis topic
This doctoral thesis focuses on trust in artificial intelligence. It aims to design and develop an approach to address the main issues raised during the design phase of an AI, such as issues of bias, explainability and fairness. These questions have started to gain importance in the literature and have become a necessity in the same way as the performance of algorithms. Existing works do not deal with these aspects jointly. The originality of this thesis is to address these issues by starting with the data collection phase while examining the risk of bias. Subsequently, the model construction phase will be carried out by addressing the discrimination issues. Finally, the decision-making phase will be dealt with by making the decisions explainable.

This thesis work is part of the theme of the LINEACT CESI laboratory. It is the result of research work in “AI and applied data sciences” of axis 2 “digital systems” of theme 2 “Engineering and digital tools” of the LINEACT laboratory. The work will be carried out around interpretable artificial intelligence and fairness for decision support. In particular, the challenge of predictive maintenance solved by artificial intelligence has an important return on industry 4.0; but this contribution must be followed by a phase of explainability of the decisions to reinforce the confidence of these AI techniques. This thesis work will contribute to the development of this context and will propose advanced solutions.


Organization of the thesis
Attached doctoral school: ED SMI (Engineering Sciences)

Your Recruitment
Its Modalities: on file and interview.

Please send your application to Dr. Genane YOUNESS gyouness[a]

Your application should include:
· A detailed Curriculum Vitae. In the event of a break in the university course, please give an explanation;
· A cover letter explaining your motivations for pursuing a doctoral thesis;
· The results of the MASTER 1 and 2 and the corresponding report cards;
· Letters of support from outside the host laboratory (2 maximum).
Please send all the documents in a zip file entitled NAME

Your skills :
Scientific and technical skills:
· Knowledge of machine and deep learning, artificial intelligence and probability/statistics.
Fluency in algorithms and programming Python, R, etc.
· Good writing in English in order to aim for the publication of a manuscript.

Social skills :
· Be autonomous, have a spirit of initiative and curiosity,
· Know how to work in a team and have good interpersonal skills,
· To be rigorous


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In 2019, 24,000 learners, including more than 14,000 in apprenticeship, were trained in the jobs and skills of tomorrow. Since its creation, CESI has forged close links with companies and regions, in particular thanks to its presence throughout France with 25 campuses, designed as real places for living, exchanging and learning.

A pioneer in France in the development of learning and active teaching methods, focused on innovation and technology, CESI operates in all sectors of activity in order to meet the challenges of employability and the skills needs of companies. . CESI trains in the professions and skills of tomorrow in industry and services, the building and the city of the future by mobilizing, on the one hand, the basic sciences and the human sciences and, on the other hand, support systems professional. It also carries out research activities in its Digital Innovation Laboratory (LINEACT CESI).

Further information

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PhD student in Artificial Intelligence M/F CESI Nanterre CDD

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