At NAVER LABS, we consider that human-centric search and recommendation interfaces are key to enabling a world of Ambient Intelligence. In this new world where location and context are understood, digital technologies will proactively propose and recommend activities, places and things to people, helping them interact and navigate in the physical environment. Such digital recommendation and guidance should be as seamless and natural as possible. The main objective of the Search and Recommendation team at NLE is to translate this ambition into the design of context-aware, personalised and anticipatory search and recommendation modules.

We’re looking for a motivated researcher to join, at a post-doctoral level, a project on Multimodal Information Retrieval. The objective of the project is to improve an already deployed large-scale multimodal search system. The work will include the development of adaptive strategies based on the query complexity, as well as the design or learning of robust cross-modal representations for both textual and visual units.

Our research is carried out with the ‘NAVER’ search group responsible for the world’s 5th biggest search engine. This provides research opportunities that go far beyond the traditional Information Retrieval framework.

We encourage participation in the academic community. Our researchers collaborate closely with universities and regularly publish in venues such as ACL, EMNLP, KDD, SIGIR, ECIR, ICML and NeurIPS.


Naver Labs Europe


Required Skills:

· Ph.D. in Information Retrieval (IR), Recommender Systems or machine learning.

· Knowledge of latest developments in statistical and deep learning IR

· Good publication record in top-tier information retrieval or machine learning conferences.

· Strong development skills, preferably in Python and knowledge of deep learning frameworks

Preferred Skills:

· Previous experience in Multimodal (text/image) Information Retrieval and/or Image Search

Educational level:

Ph. D.

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