In recent years with the raise of preprint platforms (eg. https://arxiv.org/, https://www.biorxiv.org/
), the amount of new scientific publications has increased exponentially. It is difficult for a young resercher to to face such amount of information. Moreover, most of these publications have not been peer-reviewed, and therefore it is very hard to assess the importance and credibility of certain publications for somebody who is new to the domain.
In the context of pandemia, quick access to new publications is extremely important, as well as the critical view on the scientific research. The goal of this internship is to develop tools that could assist domain experts in scientific monitoring task.
The challenges we face are:
- Scarce training data; the student will explore knowledge transfer techniques from existing datasets and pretrained models;
- Adaptation techniques to previously unseen domain (zero-shot domain adaptation)
The proposed tools will be benchmarked in the context of Bibliovid project ( https://bibliovid.org/
). The goal of the Bibliovid project is to perform monitoring and analysis of COVID19-related publications.
- Experience with deep learning models
- Programming experience in one deep learning framework, preferably Pytorch
- Experience with NLP application would be a plus
You can apply for this position online. Don't forget to upload your CV and cover letter before you submit. Incomplete applications will not be accepted.
Due to the changing travel restrictions related to COVID-19, it may not be possible to host candidates from certain regions. This will depend on the conditions at the specific starting date of the internship.
About NAVER LABS
NAVER LABS Europe has full-time positions, PhD and PostDoc opportunities throughout the year which are advertised here and on international conference sites that we sponsor such as CVPR, ICCV, ICML, NeurIPS, EMNLP etc.
NAVER LABS Europe is an equal opportunity employer.
NAVER LABS are in Grenoble in the French Alps. We have a multi and interdisciplinary approach to research with scientists in machine learning, computer vision, artificial intelligence, natural language processing, ethnography and UX working together to create next generation technology and services that deeply understand users and their contexts.