Job offer

• PDF document including the following information: surname, first name, nationalitý, date of birth, thesis title and directors, date and place of thesis defense, discipline, current position, areas of specialization and applications (if any). A project for integration at PR[AI]RIE-PSAI within PSL in both teaching and research. Depending on your specialty, experience, and projects, indicate the types of teaching you could provide, as well as the research project you would like to develop and the institutions, laboratories, or research teams you would like to join.
• Letter of motivation covering both your teaching and research projects (2 pages maximum).
• PhD. Diploma.
• Several letters of recommendation may be included.

The range of courses offered by the Paris School of AI at PSL University, as part of the PR[AI]RIE-PSAI IA-Cluster, covers all levels from bachelor to doctorate for AI specialists and non-specialists (AI+X) in initial and continuing education. Teaching is in French or English, depending on the course. The people recruited will mainly be involved in the following courses:

• International Bachelor of Science in AI ;
• Double Licence IA et science des organisations (Double Bachelor's degree in AI and organizational science), Bachelor Degree or equivalent

Skills/Qualifications

Minimal education level: PhD. French or International

Required field(s) of studies: Core areas of AI (mathematics or computer science) or application areas (physics, astrophysics, chemistry, life sciences, cognitive sciences, etc.).

Minimum level of experience required: post-doctoral fellow or young researcher

Language requirements:

• English, C1-C2 CECRL level
• French, appreciated but not required

Languages ENGLISH

• CPES Sciences des données, Art et Culture (Data Science, Art and Culture) ;
• Master IASD (Master in AI and Data Science) ;
• Master IA et Société (AI and Society) ;
• Programme transverse Data (Transverse program in Data Science).

The people recruited will need to be able to adapt to a wide range of students and teach in AI. Most of the teaching required concerns the "core of AI":

• from introduction to machine learning to advanced statistical learning, deep learning, reinforcement learning, ...
• natural language processing, computer vision, data science, ...

More specialized profiles are also in demand:

• AI for science: teaching the use of AI for different scientific disciplines to non-specialists;
• computational social sciences:

• fundamental concepts and theories of the social sciences,
• basic principles of human behavior and decision-making,
• social change and cultural dynamics,
• public policy implementation and evaluation,
• causal inference methods (e.g. regression discontinuity, difference-in-difference, instrumental variables, randomized controlled trials)

• ethics, governance and regulation:

• ethics of AI and algorithmic decision-making,
• justice, fairness and bias in AI systems,
• regulation and governance of AI, including global policy frameworks,
• societal impact of AI, with a focus on economic, cultural and political implications.