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 applications from research scientists at all levels (from post-docs to senior scientists) to join the Search & Recommendation team. Our main research themes are articulated around the development of novel Interactive Machine Learning techniques for dynamic adaptive Search and Recommendation systems. We’re targeting large-scale on-line applications in a variety of domains such as Ambient Intelligence, News Recommendation and Multimodal/Multilingual Search, with a particular attention to FAT (Fairness, Accountability and Transparency) aspects.
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
• Ph.D. in Information Retrieval (IR), Recommender Systems (RS) or machine learning (ML).
• Knowledge of latest developments in IR and RS, especially with deep learning
• Good publication record in top-tier IR, Recommender Systems, or Machine Learning conferences.
• Strong development skills, preferably in Python and knowledge of deep learning frameworks
• Previous experience in applying sequential decision making under uncertainty (MDP, POMDP, reinforcement learning, …) to Search and Recommendation
• Good knowledge of FAT-ML