Tenured positions for junior and senior research scientists at INRIA Lille in the Magnet Team
- There will be openings at INRIA Lille next year. Topics cover machine learning for graphs, information networks and for natural language processing to join the Magnet Team
- A successful applicant should have a strong machine learning and/or NLP background with a track record of top-tier research publications, including relevant conferences (e.g., ICML, NIPS, COLT, AISTATS, UAI, ACL, EMNLP).
- The recruitment process will begin in January or February. Do not hesitate to contact firstname.lastname@example.org and email@example.com for further information.
Check inria websites! Calendar and offers will be published soon.
- Main page (general information about all openings): http://www.inria.fr/en/institute/recruitment/offers
- Working as a researcher at Inria: http://www.inria.fr/en/institute/recruitment/join-us/working-as-a-researcher-at-inria#section0
- FAQ: http://www.inria.fr/en/institute/recruitment/offers/young-graduate-scientist/faq
Full time researchers
- and INRIA have also full time permanent research positions.
- CNRS campaign has started: DL Jan. 6th.
- INRIA campaign will start in January
- INRIA has also starting and advanced research positions.
- Internship (Master): Efficient Peer Discovery for Decentralized Machine Learning
- Internship (Master): Decentralized Algorithms for Privacy-Preserving Linear Regression
- Internship (Master): Spectral Graph-Based Methods for Learning Cross-Lingual Word Embeddings
- Internship (Master): Analysis of Word Semantic Change in Political Texts through Spatio-Temporal Word Embeddings
- Master project (or short Internship) Interfacing graph mining and RDF
- Master project and/or master internship: networked statistics evaluation (with LINKS)
- Internship (Master) Sampling graphs subject to subgraph density and degree distribution constraints
- Internship (Master) Algorithms for counting subgraph densities in small world graphs
- Internship (Master) Identity management for decentralized learning communities
- Internship (Master) A privacy-friendly smartphone data management tool
- Internship (Master) Models of information network noise
- Internship (Master) How to Collaboratively Learn to Taste Beer — Online Algorithms for Decentralized and Personalized Machine Learning on Graphs
PhD, PostDoc and engineer offers
We are constantly searching for candidates with good skills in machine learning. Don’t hesitate to contact us!
- Representation Learning for Privacy-Preserving
- Private distributed learning
- Decentralized Machine Learning on Graphs
- Machine learning and Natural Language Processing
In order to assess your application, the following documents are very useful:
- A curriculum vitae in English (or French)
- Scans of official transcripts of relevant BSc/MSc courses, if not in English, French, Dutch or German with a translation in English or French.
- A motivation letter in English (or French), typically at most half a page, providing
- Either a (possibly ranked) list of one or more of our open positions you are interested in, or a specification of the subject(s) you want to work on with a detailed motivation on how this fits in the research strategy of our lab.
- the source (e.g. URLs of web pages or citations to papers) where you found information on our lab or its open positions.
- why you feel your background fits these research topics. Please do not copy-paste your CV, instead list what you know about the position you are applying for and for each topic/requirement/… explain why you think you qualify.
- in case you already have funding for your PhD research / internship / …, or an application for such funding is pending, clearly specify the details of the funding schemes.
- A sample of scientific (and/or technical (engineers)) writing. Provide one or more documents authored by (if possible only) yourself, demonstrating your skills in English, mathematics and scientific writing. Published documents are preferred. If published in a peer-reviewed venue, you may choose to add the reviews. Examples include a master thesis, a research article, a project report, a documented github contribution, etc. If you send a long list (more than 5 items) of earlier work, please provide a paragraph saying which items are most relevant for your application. If no documents in English are available, please provide documents in another language.