Computer Science BUT (Bachelor of Technology)
Location : Villetaneuse IUT
The successful candidate will be responsible for teaching computer science within the BUT (Bachelor of Technology) program at the Computer Science Department of the IUT (University Institute of Technology), Université Sorbonne Paris Nord. This role covers both initial training and apprenticeships.
The Villetaneuse IUT is located on the Villetaneuse campus, in the immediate vicinity of the Paris Nord Computer Science Laboratory (LIPN).The pedagogical content follows a national curriculum (https://www-info.iutv.univ-paris13.fr/programmeBUTINFO.pdf). Teaching needs specifically focus on data processing, ranging from database design to software development quality. As the department offers courses related to Artificial Intelligence, expertise in this field would be highly valued. The successful candidate is expected to actively participate in the collective life of the department. In particular, in the short to medium term, they will be responsible for supervising internships and work-study students, participating in the organization of SAé (Learning and Assessment Situations), becoming a course coordinator, and potentially taking on broader pedagogical responsibilities.
Contacts :
Paris Nord Computer Science Laboratory (LIPN - UMR CNRS 7030) The LIPN is seeking to recruit an Assistant Professor to expand its cross-disciplinary Data Science research area, with a priority focus on strengthening the research themes of the Machine Learning and Applications (A3) team. Candidates whose profiles align with the Knowledge Representation and Natural Language (RCLN) team will also be considered. The A3 team is organized around three axes focusing on major, complementary machine learning challenges: learning from data and learners; relational and graph-based learning; and meta-learning and structure learning. Each of these axes combines fundamental and applied research, supported by collaborative academic and industrial projects. We are looking for candidates who have made theoretical and/or applied contributions to Machine Learning and who can strengthen one or more of the team’s three research axes (no order of priority). The RCLN team brings together expertise in Natural Language Processing (NLP) and Knowledge Graphs, as well as data mining and machine learning related to these two fields. The team plays an active role in the leadership and research of the LabEx “Empirical Foundations of Linguistics” (EFL). We welcome applications focusing on machine learning for NLP or for knowledge graphs.
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