Job : Postdoc (18 months), Deep Learning for opinion mining in human testimonials related to industrial accident, LITIS (Rouen)



  • *Post-doctoral position: Deep Learning for opinion mining in human testimonials related to industrial accident*


  • The Machine Learning team at the LITIS laboratory, the computer science laboratory of the University of Rouen Normandy, is looking for a post-doctoral researcher on a 18-months contract, starting as soon as possible. The position will be financed by the ANR research project CATCH (french acronym for "Automatic Understanding of Human Sensors Testimonials"), which involves the R&D center of the company Saagie, specialized in B2B DataOps solutions, Atmo Normandie, one of the approved French air quality monitoring associations and LITIS.


  • *Location* : LITIS lab., University of Rouen Normandy, Rouen, France


  • *Duration* : 18-months, starting as soon as possible


  • *Salary* : ~2300€ / month (before income tax), including social security coverage in line with French regulations


  • *Applications* : open from 01/09/2021 to 31/12/2021


  • *Keywords*: Deep learning – Natural Langage Processing – Sentiment Analysis / Opinion Mining


  • *Scientific context:* The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take into account and automatically exploit the multitude of human testimonies related to an industrial accident and its consequences on the environment and health.


  • By involving the population in the collection and analysis of data, particularly through social networks, and by providing effective means for interpreting this data, the proposed solution should contribute to providing answers to the worrying problem of industrial accidents and their consequences.


  • The overall objective is first to draw up a precise cartography of the nuisances in order to follow the propagation and the evolution of the phenomena in time, and then to analyze and characterize the sentiment of the population and its evolution throughout the crisis.


  • To do so, we intend to exploit testimonials collected on the ODO platform of Atmo Normandie, which combines these testimonies with geographical information, in conjonction with data extracted from the micro-blogging platform Twitter.


  • Since these data are primarily texts, state-of-the-art approaches from the Natural Language Processing (NLP) field are favored, in particular, self-supervised deep learning methods such as Transformers that are known to be the most performant today for a wide range of NLP tasks.


  • **Research goals:* The objective of this research work is twofold: 1. The automatic generation of a map of nuisances around the site of an industrial incident to monitor the propagation and the evolution of the phenomena over time.


  • 2. The automatic recognition of the population's perception and its evolution throughout the crisis.


  • Related to these tasks, the post-doctoral researcher will be in charge of proposing solution for: - extracting and linking twitter data with testimonials from the ODO dataset, which is fully labelled and associates textual testimonies with geographical data. The interest in establishing this link is to be able to enrich the data from the ODO platform to refine the mapping of nuisances in real time. This could be achieved for example, by using pseudo-labelling techniques1 or Constrative Representation Learning methods which have recently been applied to text data2.


  • - detecting in all the testimonials collected from Twitter or from the ODO platform, the presence (or absence) of several pre-identified emotions (e.g. surprise, fear, anger, sadness, disgust, etc.), several of which can be expressed at the same time.


  • This research work will therefore involve being familiar with the state-of-the-art NLP deep learning methods and in particular with their applications to sentiment analysis and opinion mining tasks. It will also require experience with the use and exploitation of data from Twitter in a data science context.


  • **Application:* The successful applicant will: - possess or be in the process of obtaining a Ph.D. in computer science or applied mathematics, with a focus on machine learning or data mining.


  • - have strong programming skills (Java, Python, etc.) and in-depth understanding of statistics and machine learning.


  • - have already worked with deep learning architecture dedicated to texts (RNNs, Transformers, etc.) and/or images (CNNs, FCNs, GANs).


  • - have a productive publication record.


  • Your application should include: - curriculum vitae


  • - statement of past research accomplishments, career goal and how this position will help you achieve your goals


  • - two representative publications


  • - contact information for at least two references


  • Application must be sent to : - Simon BERNARD, University of Rouen Normandy, [email protected]


  • - Clément CHATELAIN, INSA Rouen Normandy, [email protected]


  • - Alexandre PAUCHET, INSA Rouen Normandy, [email protected]


Postdoc (24 months), Representing and enriching events in knowledge graphs, IRIT (Toulouse)



  • Post-doctoral position


  • Representing and enriching events in knowledge graphs


  • Keywords: machine learning on graphs, knowledge graph, event prediction


  • Context: XP-event project (2021-2024)


  • An event is defined as “anything that happens, anything that fits over time”: meetings, phone calls, purchases, but also business buyouts, change of management, health crises, etc. The events are shared at through various communication channels that can be private (internal documentation, emails, Slack, Teams, phone, etc.) or public (press, Twitter, Facebook, etc.). Knowledge of these events is essential for humans to make decisions which themselves will have an impact on future events. Many innovative applications can benefit or even emerge from a technology capable of extracting events from various sources, representing them, aggregating them and exploiting them to predict future events. We can for example cite: anticipating demand for sanitary products, the supervision of cultural, advertising or festive events, but also the study of competition, the study of commercial markets, etc.


  • One of the main obstacles to the deployment of these applications is the excessively high cost of their development when it is carried out on an ad hoc basis by competing players. The XP-Event project proposes to respond to this difficulty by setting up a common base for all the applications organized around the notion of event. This project is led by a consortium naturally formed by two companies (GeoTrend and Emvista) and a research team from the IRIT laboratory sharing this vision and each having significant scientific and technological heritage in the field.


  • Position Description The candidate will contribute to the tasks in which IRIT is involved, and will be more particularly in charge of realizing and implementing the proposed solutions. The first task concerns the representation of event graphs. The first task will be to define an adapted ontology and a process allowing to exploit it to access or represent in RDF the graphs of the industrial partners of the project, which are graphs of quite different nature.


  • The second task aims at defining a process to evaluate the quality of the event graphs. Evaluation will be based on the ontology structure as well as on reasoning from the knowledge graph. The third task concerns the enrichment of these graphs. Two types of approaches will be implemented in the project, and for each of them research will be needed to advance the state of the art. The first approach consists in extracting information from texts. Each of the industrial partners already has its own processing chain that it will improve and unify. The second approach consists in exploiting the current state of a graph but also the structure of an event in the ontology to suggest the addition of new nodes or new relations to the graph. This approach will be implemented through learning algorithms from graph.


  • Requirements for this position Applicants are required to have a PhD in computer science, and strong background ideally in two areas of artificial intelligence: semantic web technologies (ontology engineering, linked data management and querying, SPARQL, SHACL, RuleML, ...), and machine learning from graphs and vector representations, recursive neural networks, etc.


  • Good programming skills (Python, OWL API) and experience in participating in collaborative projects is required. In addition, the candidate must have a taste for innovation, and the ability to dialogue and collaborate with industrial partners. Experience in managing graph warehouses (Virtuoso, Strabon, Neo4j...) is desired. Fluency in written / spoken English is required too. Fluency in French language will be a plus.


  • Work environment


  • Location : Institut de Recherche en informatique de Toulouse (IRIT) - UPS, 118 Route de Narbonne F-31062 Toulouse Cedex - France and et UT2J, 5 allées Antonio Machado 31300 Toulouse


  • Duration: 24 months – starting on January 1st, 2022


  • Host team: MELODI https://www.irit.fr/en/departement/dep-artificial-intelligence/melodi-team/


  • The candidate will work with four academic researchers from MELODI (F. Benamara, Ph. Muller, N. Aussenac-Gilles and N. Hernandez). He will collaborate with the partner companies in the project, namely Geotrend, located in Toulouse, and Emvista, located in Montpellier.


  • Income: between 2300 and 2800 euros before taxes (brut) monthly according to past experience


  • How to apply? Applicants should send their application files before November 15th 2021 to the contact persons listed here below. Application files should contain at least a full Curriculum Vitae 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.


  • Applicants should contact: N. Aussenac-Gilles ([email protected])


  • N. Hernandez ([email protected])


Thesis funding at LS2N - University of Nantes - Semantic Web



  • Hello everyone, We are looking for a candidate to do a thesis in Nantes in the field of the semantic web.


  • Start date : January 2022 at the latest.


  • Subject : Integrate and explore related educational resources.


  • Supervisors : Patricia Serrano Alvarado and Emmanuel Desmontils.


  • Keywords : exploration of knowledge graphs, SPARQL queries, ontologies, query relaxation.


  • Candidate profile : Master in computer science or equivalent (classified in the first third); good skills in Java programming, JavaScript, web applications, Python; good bases on semantic web technologies (RDF, OWL, SPARQL); good oral and written communication skills in English.


  • Funding over 3 years (Labex Cominlabs) : Gross salary € 1,866. Net salary ~ 1500 €.


  • To apply : send your application to [email protected] with a detailed curriculum vitae, your transcripts, a list of two references and your bachelor's / master's reports in PDF format. Applications will be received until the position is filled.


  • Subject description : https://bit.ly/2ZZq2w0


  • Thank you for disseminating this offer to your students.


  • Best regards, Patricia


Post-doctoral position - Representing and enriching events in knowledge graphs



  • Key words: machine learning on graphs, knowledge graph, event prediction


  • Context: XP-event project (2021-2024) An event is defined as “anything that happens, anything that fits over time”: meetings, phone calls, purchases, but also business buyouts, change of management, health crises, etc. The events are shared at through various communication channels that can be private (internal documentation, emails, Slack, Teams, phone, etc.) or public (press, Twitter, Facebook, etc.). Knowledge of these events is essential for humans to make decisions which themselves will have an impact on future events. Many innovative applications can benefit or even emerge from a technology capable of extracting events from various sources, representing them, aggregating them and exploiting them to predict future events. We can for example cite: anticipating demand for sanitary products, the supervision of cultural, advertising or festive events, but also the study of competition, the study of commercial markets, etc. .


  • One of the main obstacles to the deployment of these applications is the excessively high cost of their development when it is carried out on an ad hoc basis by competing players. The XP-Event project proposes to respond to this difficulty by setting up a common base for all the applications organized around the notion of event. This project is led by a consortium naturally formed by two companies (GeoTrend and Emvista) and a research team from the IRIT laboratory sharing this vision and each having significant scientific and technological heritage in the field.


  • Position Description The candidate will contribute to the tasks in which IRIT is involved, and will be more particularly in charge of realizing and implementing the proposed solutions. The first task concerns the representation of event graphs. The first task will be to define an adapted ontology and a process allowing to exploit it to access or represent in RDF the graphs of the industrial partners of the project, which are graphs of quite different nature. The second task aims at defining a process to evaluate the quality of the event graphs. Evaluation will be based on the ontology structure as well as on reasoning from the knowledge graph. The third task concerns the enrichment of these graphs. Two types of approaches will be implemented in the project, and for each of them research will be needed to advance the state of the art. The first approach consists in extracting information from texts. Each of the industrial partners already has its own processing chain that it will improve and unify. The second approach consists in exploiting the current state of a graph but also the structure of an event in the ontology to suggest the addition of new nodes or new relations to the graph. This approach will be implemented through learning algorithms from graph.


  • Requirements for this position Applicants are required to have a PhD in computer science, and strong background ideally in two areas of artificial intelligence: semantic web technologies (ontology engineering, linked data management and querying, SPARQL, SHACL, RuleML, ...), and machine learning from graphs and vector representations, recursive neural networks, etc. Good programming skills (Python, OWL API) and experience in participating in collaborative projects is required. In addition, the candidate must have a taste for innovation, and the ability to dialogue and collaborate with industrial partners. Experience in managing graph warehouses (Virtuoso, Strabon, Neo4j...) is desired. Fluency in written / spoken English is required too. Fluency in French language will be a plus.


  • Work environment Location : Institut de Recherche en informatique de Toulouse (IRIT) - UPS, 118 Route de Narbonne F-31062 Toulouse Cedex - France and et UT2J, 5 allées Antonio Machado 31300 Toulouse


  • Duration: 24 months – starting on January 1st, 2022


  • Host team: MELODI https://www.irit.fr/en/departement/dep-artificial-intelligence/melodi-team/


  • The candidate will work with four academic researchers from MELODI (F. Benamara, Ph. Muller, N. Aussenac-Gilles and N. Hernandez). He will collaborate with the partner companies in the project, namely Geotrend, located in Toulouse, and Emvista, located in Montpellier.


  • Income: between 2300 and 2800 euros before taxes (brut) monthly according to past experience


  • How to apply? Applicants should send their application files before November 15th 2021 to the contact persons listed here below. Application files should contain at least a full Curriculum Vitae 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.


  • Applicants should contact: N. Aussenac-Gilles [email protected] and N. Hernandez [email protected] --


Ground Segment Engineer



  • Context


  • ISAE-SUPAERO is an international benchmark institution for higher education and research in aeronautics and space.


  • The CREME project aims to design and produce a flight model of a nanosatellite in the format CubeSat type 3U. This CubeSat carries a payload of the radiation monitor type designed and developed by ONERA.


  • The partners of this ERDF Region Occitanie project are ONERA, ISAE-SUPAERO, both founding members of the University Space Center of Toulouse (CSUT) and project leaders, and 3 SMEs in the space sector (3D Plus, EREMS and TRAD).


  • As part of this project, ISAE-SUPAERO is recruiting an Engineer Ground segment.


  • Job Description


  • The objective of this position is to participate in the development of the Soil CREME Segment.


  • Concrete- ment, the duties of the position will consist of designing, developing, integrating and validating the Segment


  • Floor of the CREME project. Traditionally, a ground segment is broken down into three components:


  • a control center, a ground station and a mission center.


  • As part of the CREME project, Onera is responsible for the mission center and ISAE for the other two components, but must also ensure overall consistency.


  • The tasks on which the Ground Segment Engineer will be called upon to consist of therefore attempt to define, implement and implement the functionalities of the control center


  • CREME in accordance with the mission specifications and in cooperation with the project team;


  • To define and assist in the implementation of the necessary network architecture in cooperation with the IT services of ISAE-SUPAERO; to define and ensure the consistency of the interface between the control center, the ground station and the mission center, in cooperation with Onera.


  • The recruited person will work in collaboration with the technical teams of the institute and a of the challenges of the position is to ensure the sustainability of all of this work for future nanosatellite soil segment projects.


  • Training and skills required:


  • - Graduated from a Bac + 5 or engineering school or university


  • - Computer or telecommunications specialty


  • - Good mastery in software development (C, C ++, Java, Python, web technologies ...)


  • - Knowledge of space ground segment and systems or space systems specifications


  • - Ability to implement a hardware architecture in support of application software and in systems integration within a project team


  • Most :


  • - Notions in RF


  • - Experience in the fields of space engineering, design or development space systems, more particularly on CubeSat projects.


  • Additional information: Thibault Gateau ([email protected]) Send CV and cover letter to [email protected] with in subject: "[creme] application for the position of Ground Segment Engineer"


  • Type of contract: CDD


  • Contract duration: 12 months


  • Position open as soon as possible


  • Salary according to CV and experience


Postdoctoral Researcher in Deep Learning for Video Analysis



  • The ImViA lab at University Burgundy (Dijon - France), together with Honda Research Institute (Japan), invites applications for postdoctoral research positions in deep learning for video analysis.


  • The postdoctoral researcher will work on improving existing deep learning models to estimate physiological signals from the video signal. Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate and other vital signs by analyzing subtle skin color variations using regular cameras (see [1] for an interesting review).


  • More recently, end-to-end approaches based on deep learning have also been used. We will seek to extend existing work by improving current models, focusing on night vision applications. The candidate will take part in ongoing projects and possibly initiate new research within the team.


  • The postdoctoral researcher will work in Dijon - France in collaboration with researchers from the Honda Research Institute in Japan. This fellowship has a duration of 12 months with possibility of extension. As part of this postdoc, we can offer generous support for professional travel and research needs.


  • We are seeking a highly qualified and motivated candidate with a Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering, or a closely related field with a relevant scientific track record on significant computer vision conferences/journals as well as experience on deep learning techniques and frameworks.


  • Interested candidates should submit their CV, letter(s) of reference, and a brief research statement describing their background and research interests and how they align with the project emailed to Yannick Benezeth ([email protected]). The call will remain open until the position is filled. The postdoc contract will start as soon as possible.


  • University website: https://en.u-bourgogne.fr/


  • Lab website: https://imvia.u-bourgogne.fr/


  • Yannick Benezeth professional website: https://sites.google.com/view/ybenezeth


  • https://scholar.google.fr/citations?user=JZ6tlZwAAAAJ


Fully Funded PhD Position in Biotech Data Science



  • Dear Colleagues,


  • Ghent University Global Campus in Korea has a vacancy for a Research & Teaching Assistant (PhD Candidate) in the area of biotech data science (biological sequence analysis or biomedical image understanding), starting from March 1, 2022 (open to negotiation). This is a 1-year full time position that is renewable three times (upon favorable evaluation), for a total period of maximum 6 years.


  • The candidate will work as a PhD Candidate at the Center for Biosystems and Biotech Data Science of Ghent University Global campus in Korea, under the guidance of two to three doctoral advisors. Furthermore, the candidate will be able to spend time at the home campus in Ghent during their PhD studies (at IDLab - ELIS). For non-Korean applicants, free student accommodation and a yearly travel budget are foreseen. Ghent University Global Campus is an equal opportunities employer.


  • The PhD Candidate is expected to spend about 50% of their assignment on supporting the department in teaching undergraduate courses in informatics and bioinformatics. These activities include for the most part the supervision of exercise sessions and computer labs, as well as assisting in the development of new course materials (e.g., assignments and exam questions), the grading of assignments and exam questions, and the supervision of yearly bachelor projects and occasional internships.


  • Apart from helping out with teaching, the PhD Candidate is expected to perform research on the topic of (deep) machine learning, either targeting applications in the domain of biological sequence analysis (e.g., structural and functional genome annotation, protein structure prediction) or applications in the domain of biomedical image understanding (e.g., medical video analysis).


  • In this context, the PhD Candidate is expected to complete a doctoral research proposal of about 10 pages within the first six months of their appointment, containing a literature review, a set of research objectives, a work plan (work packages and a Gantt chart), and a publication plan. This doctoral research proposal is to be approved by the GUGC Campus Council (this approval is a necessary condition for the first contract extension).


  • More detailed information about this fully funded position can be found at the URL below:


  • https://www.jobs.ac.uk/job/CKC778/research-and-teaching-assistant-phd-candidate


  • Best regards, Wesley De Neve


M2 internship offer: Reasoning on imperfect data



  • Basic Information Duration: 4-6 months, ideally between February - July 2022


  • Supervisors: Meghyn Bienvenu (LaBRI, Bordeaux), Camille Bourgaux (DI ENS, Paris)


  • Host lab: LaBRI (Laboratoire Bordelais de Recherche en Informatique)


  • Candidate Profile


  • This internship is best suited to candidates who have prior experience with knowledge representation and reasoning (especially: description logics, non-monotonic reasoning), database theory, or Semantic Web (ontologies).


  • Candidates must demonstrate familiarity with propositional and first-order logic and basic notions of computational complexity. Strong English skills are desired.


  • How to Apply


  • Candidates for the Master's internship should contact the two supervisors by email:


  • • Meghyn Bienvenu ([email protected])


  • • Camille Bourgaux ([email protected])


  • The email should include a CV, course transcripts (last two years), and a short description of how the internship topic relates to their prior experience and research interests.


  • The position will remain open until a suitable candidate is found. However, for full consideration, applicants should get in touch by November 30, 2021.