Post-Doc position available at LS2N, Nantes, France with mobility at NII Tokyo, Japan
Title: Combining graph embedding and topic modeling for ontology / KG learning from large scale data
Key words : Topic modeling, Knowledge graph learning, Ontology learning, Graph embedding, Deep learning
Description . Ontology learning from the web data is a major challenging topic within the semantic web field and many approaches have been developed to tackle it. However, due to sparsity and heterogeneity of data, they lack to provide good quality results with a high semantical relevance for humans. The post-doc work aims to define a new approach for ontology learning / knowledge graph learning by incorporating embedded knowledge graphs in a clustering technique (topic modeling) dealing better with the sparsity and the heterogeneity of texts available in the Web and the semantical relevance of the results.
The research domain of this post-doc position is model learning, linked data and graph embedding for ontology / knowledge graph learning from texts. Model learning / Topic modeling is one of the area expertise of the DUKe team (Data User Knowledge) of LS2N, one of the France's leading public research labs in digital sciences. Linked data, graph building from texts and knowledge graph embedding are fields of expertise of the Japanese Ichise Laboratory from the National Institute of Informatics (NII), one of the leading research institute in Japan.
Duration : 12 months from (1 January 2022 -31 December 2022) including a mobility of 3 months in Japan.
Localization : Polytech Nantes, France, Ichise Laboratory, Tokyo Japan
Salary : € 2,900 gross monthly + mobility expenses in Japan, during three months, about 350,000 yen / month .
Application : Candidates should have a PhD in computer science or applied mathematics, with strong experience in machine learning and related coding ecosystems in python. A background in semantic web and probability / statistics would be a plus.
Applicants should send a full CV including a complete list of publications and completed projects, a cover letter, and letters of recommendation or the names of two people who have worked with them.
Contact: Mounira Harzallah ( [email protected] , Fabrice Guillet [email protected] ), DUKe, LS2N, France
Hello, Please find attached a post-doc proposal on the participatory design of models and simulation, carried by the SELMET and SENS units (CIRAD, INRAE, IRD, UPVM).
It's all in the ad. Do not hesitate to broadcast on your lists.
Best regards Jean-Pierre Müller
Required Skills Doctorate in Computer Science
Mastery of software engineering techniques and in particular IDM (xtext, Sirius)
Knowledge of modeling and in particular of multi-agent systems
Openness to participatory co-design approaches
Interdisciplinary experience
Required qualities Ability to listen, ability to abstract, autonomy, teamwork. Mastering French and English, spoken and written.
To inquire [email protected] or [email protected]
To apply You must send i) a detailed CV, ii) a cover letter and iii) two letters of recommendation to [email protected] before December 5, 2021.
Post-doctoral position at IRIT: Data Linking ** - Context: ANR project DACE-DL (DAta-CEntric AI-driven Data Linking) Data linking is the scientific challenge of automatically establishing typed links between the entities of two or more structured datasets.
A variety of complex data linking systems exists, evaluated on public benchmarks. While they have allowed for the generation of vast amounts of linked data in the context of various dedicated projects, data generic systems often have limited applicability in many real-world scenarios, where data are highly heterogeneous and domain-specific.
DACE-DL targets a paradigm shift in the data linking field with a data-centric bottom-up methodology relying on machine learning and representation learning models. We hypothesize there exists a finite number of identifiable and generalisable linking problem types (LPTs), that we need to categorize and analyse to provide better linking results.
- Topic: Data collect, consolidation, and data linking systems modularization
This research is articulated in two main tasks. The first task consists in (1) carrying out an in-depth analysis of the quality of the existing data linking datasets, identifying erroneous statements and providing a high-quality set of datasets by correcting those statements;
and (ii) generating additional links using existing high-precision linking systems on the chosen datasets. Data quality metrics such as accuracy, consistency and conciseness will be considered.
The aim of the second task is manifold : (1) to provide an inventory of publicly available and functional linking tools that are able to deal with a large spectrum of data linking problem;
(2) to propose a theoretical approach for the modularization of these tools into atomic modules easy to combine in order to build more complex solutions in a linking ecosystem; (3) to make the produced modules available to the data linking community.
To do the modularization at scale, we plan to call upon unsupervised ML algorithms, enhanced by a human-in-the-loop approach. The objective is to provide a set of correspondences between the modules and the LPTs.
Starting period: January 2022 – duration of 24 months
* Work environment and Salary * Localization : Institut de Recherche en informatique de Toulouse (IRIT) Universite Toulouse - Jean Jaures / Maison de la Recherche, 5, allees Antonio Machado 31058 Toulouse.
Salary between 2200€ and 2700€ gross monthly depending on qualifications and situation.
* How to apply * Applicants are required to have a PhD in Computer Science, a strong background in semantic web technologies, ontology matching and data linking.
Fluency in written / spoken English is required too. A good publication record and strong programming skills will be a plus. Applications will be accepted until the position is closed. Applicants should send a full CV including a complete list of publications, a cover letter indicating their research interests, achievements to date and vision for the future, as well as either support letters or the name of 2 persons that have worked with them.
Contact: Cassia Trojahn ([email protected]) and Olivier Teste ([email protected])
Date: Wed, 10 Nov 2021 16:04:03 +0100 (CET)
From: Kévin Deturck [email protected]
Hello I have the honor to invite you to my doctoral thesis defense titled "Detection of influencers in social media".
This will take place on Thursday, November 18 at 9 a.m., at 2, rue de Lille, 75007, Paris, in the Antoine Isaac Silvestre de Sacy room (L2.05).
It will be presented in French, in front of a jury composed of:
Mr. Pascal Amsili, University Professor, University of Paris 3, Examiner,
Ms. Claudine Moïse, University Professor, UGA, Rapporteur,
Mr. Damien Nouvel, Inalco, Co-supervisor,
Mr. Patrick Paroubek, Research Engineer, CNRS, Rapporteur,
Ms. Namrata Patel, Doctor, University Montpellier 3, Co-supervisor, Guest,
Ms. Frédérique Segond, Research Director, Inria, Inalco, Director,
Mr. Mathieu Valette University professor, Inalco, Examiner.
The defense will be followed by a drink to which you are warmly welcome invited.
The size of the room being limited to 21 people, I would be grateful to you to let me know your intention to come to the address " [email protected] ".
You can also write to me if you would like to attend this remote defense, so that I can share the connection link with you distancing.
Best regards Kevin Deturck
Summary of the thesis: This is the design and evaluation of a system for automatically detect influential people in the media social, from the manifestations of their influencing action in interpersonal communications.
Approaches for detection influencers generally use either the structure of the communication between individuals, or the analysis of its content.
The theoretical framework retained in our thesis has the particularity of combine these two types of approaches for their complementarity.
We characterize the action of influencers at the level of an individual target, from its implementation to its effects, by traits discursive issues relating to messages sent by influencers than those sent by the influenced individuals.
The automatic detection of these discursive traits is done with methods in automatic language processing, based on language rules and models by machine learning.
At the level of a group, the action of influencers is characterized by their central position in a social graph which represents interpersonal actions that have taken place within this group.
The hybridity of our system consists of the use of linguistic information on discursive influence traits, automatically extracted from text messages exchanged between individuals, in order to build social graphs.
Scientific data curator – 24 months – Grenoble, France
Starting date: January 03, 2022 at the earliest
Duration: full-time position for 24 months (with a possibility of reappointment)
Deadline for Applications: November 30th, 2021
Location: The position will be based in Grenoble, France
Remote work will be possible, eg: 1 day/week
Keywords: Corpus, digital humanities, data collection
Context The NanoBubbles ERC Synergy project’s objective is to understand how, when and why science fails to correct itself. The project focuses on 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 through various channels of scientific communication (journals, social media, advertisements, conference programs, etc.) via both qualitative and digital methods.
Your contribution to the main project will be to advise on, run and/or maintain software and systems that support activity related to collection, analysis, storage and presentation of textual data and metadata.
This is an exciting opportunity to join a highly interdisciplinary research team working at the forefront of Science and Technology Studies, Digital Humanities, ethics of/in research, and nanoscience.
You will: Build corpora with data collected from heterogeneous sources (eg.: bibliographic databases like Scopus or Dimensions, full-text databases like ISTEX or open archive repositories, social networks, post-publication peer-review platforms and other online tools allowing annotations and comments…)
Process and transform data, organize data flow to database, create formal links between datasets.
Curate metadata
Develop scripts for data collection via APIs (preferably: Python, SQL, Java, R) and web scraping (e.g., HtmlUnit, Selenium)
Contribute to the development of a common vocabulary and map it to existing ontologies Implement and manage various software pipelines to support data analysis and text mining.
Help the other team members to run experiments and validate their choices
Document the data lifecycle and update the data management plan
You will work closely with PhD students, interns and researchers of the ERC project.
You will also benefit from the skills and the research environment of 2 research units: the LISIS (http://umr-lisis.fr) and the LIG (https://www.liglab.fr/en).
Qualifications Master’s degree in data science, digital humanities or computational social sciences.
Very good knowledge of English
Qualifications in corpus linguistics tools, corpus-based research, quantitative and qualitative data analysis, natural language processing or computational linguistics are deemed as a plus.
Instructions for applying Applications are expected until November 30th, 2021.
Please send CV + letter/message of motivation + grades from previous education + references for potential letter(s) of recommendation to:
Frederique Bordignon ([email protected]),
Cyril Labbé ([email protected]),
and Cyrus Mody ([email protected]).
Dear all, We offer two internship opportunities in topics related to IoT and smart spaces.
These positions are for students of Master 2 or for last-year students of an Engineering school.
You can find the related internship descriptions in the following links: - Position 1: https://gbouloukakis.com/files/jobs/eccosmarts.pdf
- Position 2: https://gbouloukakis.com/files/jobs/planmosquitto.pdf
To apply, fill in the form at https://forms.gle/s93w4KuBS3vjCAVUA and provide the following documents: - CV
- Motivation letter
- Transcripts of the last 3 years
- A course report or article written in English (if any)
Best regards, Georgios Bouloukakis
Georgios Bouloukakis, Associate Professor Dept. of Computer Science, Télécom SudParis, IP Paris, France
Email: [email protected] Web: https://gbouloukakis.com
Post-doctoral position at IRIT: Data Linking
Context: ANR project DACE-DL (DAta-CEntric AI-driven Data Linking)
Data linking is the scientific challenge of automatically establishing typed links between the entities of two or more structured datasets. A variety of complex data linking systems exists, evaluated on public benchmarks. While they have allowed for the generation of vast amounts of linked data in the context of various dedicated projects, data generic systems often have limited applicability in many real-world scenarios, where data are highly heterogeneous and domain-specific. DACE-DL targets a paradigm shift in the data linking field with a data-centric bottom-up methodology relying on machine learning and representation learning models. We hypothesize there exists a finite number of identifiable and generalisable linking problem types (LPTs), that we need to categorize and analyse to provide better linking results.
Topic: Data collect, consolidation, and data linking systems modularization
This research is articulated in two main tasks. The first task consists in (1) carrying out an in-depth analysis of the quality of the existing data linking datasets, identifying erroneous statements and providing a high-quality set of datasets by correcting those statements; and (ii) generating additional links using existing high-precision linking systems on the chosen datasets. Data quality metrics such as accuracy, consistency and conciseness will be considered.
The aim of the second task is manifold : (1) to provide an inventory of publicly available and functional linking tools that are able to deal with a large spectrum of data linking problem; (2) to propose a theoretical approach for the modularization of these tools into atomic modules easy to combine in order to build more complex solutions in a linking ecosystem; (3) to make the produced modules available to the data linking community. To do the modularization at scale, we plan to call upon unsupervised ML algorithms, enhanced by a human-in-the-loop approach. The objective is to provide a set of correspondences between the modules and the LPTs.
Starting period: January 2022 – duration of 24 months
* Work environment and Salary *
Localization : Institut de Recherche en informatique de Toulouse (IRIT) – Universite Toulouse - Jean Jaures / Maison de la Recherche, 5, allees Antonio Machado 31058 Toulouse.
Salary between 2200€ and 2700€ gross monthly depending on qualifications and situation.
* How to apply *
Applicants are required to have a PhD in Computer Science, a strong background in semantic web technologies, ontology matching and data linking. Fluency in written / spoken English is required too. A good publication record and strong programming skills will be a plus. Applications will be accepted until the position is closed. Applicants should send a full CV including a complete list of publications, a cover letter indicating their research interests, achievements to date and vision for the future, as well as either support letters or the name of 2 persons that have worked with them.
Contact: Cassia Trojahn ([email protected]) and Olivier Teste ([email protected])
As part of the European AI4T project, we are looking for a post-doctoral fellow on the subject of AI training for teachers. This is a 2-year position, based at the University of Nantes, within LS2N and more specifically the Unesco RELIA Chair (Free Educational Resources and Artificial Intelligence).
Required profile Dual competence in educational science / technology and artificial intelligence,
Excellent use of English: this is a European project and written and oral communications will be mainly in English, Autonomy.
The recruited person will have to develop educational resources to be used in teacher training (mainly secondary) in several European countries. This work will be done in consultation with European specialists, in particular in conjunction with IRCAI .
This work will be accompanied by a scientific reflection - scientifically valid - on the teaching of artificial intelligence, in primary and secondary education.
For more details, see the job description or contact [email protected] . Colin de la Higuera