[Applications closed] Postdoc position in Natural Language Processing for scientometrics (Grenoble Alps University, France)
Applications for this position are now closed
Starting date: January. 03, 2022 at the earliest
Duration: full-time position for 24 months (with a possibility of reappointment)
Salary: according to experience (from 2038€/month)
Deadline for Applications: Nov 30th, 2021
Location: The position will be based in Grenoble, France. Remote work is partly possible (e.g., 1 day a week).
Keywords: natural language processing, citation classification, citation content/context analysis, scientometrics, name entity recognition, argument mining, transfer learning, deep learning
The Grenoble Alps University has an open position for a highly motivated postdoc researcher. The successful candidate will work on the multi-disciplinary ERC Synergy research project NanoBubbles ERC Synergy (https://nanobubbles.hypotheses.org) supported by the European Research Council (ERC). The project objective is to understand how, when and why science fails to correct itself. The project’s focus is claims made within the field of nanobiology. Project members combine approaches from the natural sciences, computer science, and the social sciences and humanities (Science and Technology Studies) to understand how error correction in science works and what obstacles it faces. For this purpose, we aim to trace claims and corrections in various channels of scientific communication (journals, social media, advertisements, conference programs, etc.) via natural language processing (NLP) technics.
The challenge is to build datasets, models and tools that enable analysing how scientific papers are cited, how claims appear in scientific records and are propagated. This challenge also encompasses the analysis of the rapidly evolving ecology of online comments (on post-publication peer-review venues, for instance) complementary to conventional scientific records. The project is interested in hiring people able to contribute to one or more of the following challenges:
Leveraging existing and developing new NLP methods to retrieve citation contexts, detect – citation polarity and build citation network of scientific statements. This means not only counting citations received by a publication but also assessing and leveraging the content of both cited and citing documents, whether the citation occurs in a scientific paper, a tweet or an online comment.
Leveraging existing and developing new NLP methods to detect, track and identify named entities, claims and counterclaims in scientific publications and social media.
Take advantage of existing corpus to learn models but also build tools for creation and annotation of new datasets so as to visualise the propagation of claims and counterclaims.
The hired person will interact with PhD students, interns and researchers hired as part of the ERC project. According to his/her background he/she will work in on one or more of the above-mentioned challenges. The hired postdoc would also be expected to lead the diffusion of corpus collected through open source platforms and/or open shared tasks organisation.
The person recruited will be hosted within the Sigma and Getalp teams of the LIG laboratory (http://sigma.imag.fr/ and https://lig-getalp.imag.fr/), which offers a dynamic, international, and stimulating framework for conducting high-level multi-disciplinary research. The teams are housed in a modern building (IMAG) located in a 175-hectare landscaped campus that was ranked as the eighth most beautiful campus in Europe by Times Higher Education magazine in 2018.
The person will also be required to collaborate with several teams involved in the ERC Nanobubbles project, in particular with researchers in France from the IRIT lab (Toulouse, France), Ecole des Ponts ParisTech, University of Sorbonne Paris-Nord, CNRS, as well as researchers from in the Netherlands including Maastricht University, Radboud Universiteit and University of Twente.
The ideal candidate must have a PhD degree in Natural Language Processing, computer science.
The successful candidate should have:
- Good knowledge of machine learning techniques
- Good knowledge of Natural Language Processing, previous experience in NER, RE.
- Experience in corpus collection/formatting and manipulation.
- Strong programming skills in Python
- Excellent publication record
- Willing to work in multi-disciplinary and international teams
- Good communication skills
Instructions for applying
Applications are expected until Nov 30th, 2021 and must be addressed to Cyril Labbé (email@example.com), François Portet (firstname.lastname@example.org), Frédérique Bordignon (email@example.com).
Applications will be considered on the fly. It is therefore advisable to apply as soon as possible. The application file should contain:
- Curriculum vitae
- References for potential letter(s) of recommendation
- One-page summary of research background and interests
- At least three publications demonstrating expertise in the aforementioned areas
- Pre-defence reports and defence minutes; or summary of the thesis with date of defence for those currently in doctoral studies