You will be responsible for the data interoperability project which is interested in building a prototype of a distributed architecture for the publication and exchange of data.
This architecture will be based on open Web standards (protocols, formats, vocabularies), in order to ensure the sustainability and independence of the actors (public and private) participating in these exchanges:
the ambition is to propose a technological infrastructure for give all players control over “their” data, and limit the effects of “natural monopoly”
Apply here: https://jobs.inria.fr/public/classic/fr/offres/2022-05150
We have an open position for a postdoctoral researcher on natural language processing / information retrieval / machine learning (SCAI/BnF research program)
Starting period: autumn 2022
Duration: 12-month postdoctoral contract, renewable)
Location: Sorbonne university (ISIR lab in the MLIA team) / DataLab of the BNF
Supervision: Laure Soulier, MCF in computer science at Sorbonne University, MLIA team, ISIR. Emmanuelle Bermès, Scientific and Technical Assistant to the Director of Services and Networks at BnF.
Jean-Philippe Moreux, Scientific expert of Gallica at the BnF.
More info: https://scai.sorbonne-universite.fr/public/news/view/27d72d260c950c8d66c6/1
Context Gallica, the digital library of the BnF, contains nearly 10 million digitized documents that are freely accessible online (18.5 million visits per year). However, most users do not know that Gallica contains not only printed documents, but also photographs, sound recordings, videos, and 3D objects.
In satisfaction surveys, only a minority of users consider the search engine's answers to be relevant and a majority would like to be better guided in their searches. A recommendation system should be able to help users find their way through the mass of collections and improve the visibility of the least known.
In this project, BnF is committed to adopting a resolutely ethical approach. The exploitation of user logs must respect their privacy and guarantee both the relevance and transparency of the algorithms, avoiding the risk of filter bubbles.
The interface design is also at the heart of the approach: a trustworthy system relies on a good user experience and on the diversity and relevance of the proposed recommendations. Three lines of thought emerge:
1) based on the available data, including both user logs and collection descriptions, how to develop predictive algorithms?
2) how to integrate diversity in the recommendation algorithm while leaving the choice to the user to moderate his serendipity threshold?
3) how to build user trust in algorithm design and audit?
Main missions This project consists in working on information access in the Gallica library, from the point of view of machine and deep learning techniques. The research axes concern (1) the analysis and indexing of textual documents as well as (2) the analysis of user traces and (3) recommendation systems. We are particularly interested in multimodal techniques that allow contextualizing a document or a query based on user interactions.
The successful candidate will be responsible for: ● Implementing models to learn the semantics of textual data for the purpose of indexing them.
● Developing algorithms based on representation learning methodologies to effectively blend text and user traces.
● Reporting and presenting development work in a clear and effective manner, both for discussion with BnF experts and writing machine learning publications.
The printed book collection will be the primary focus of the program described above, but an extension to other collections with textual descriptors (in particular iconographic collections) may be considered.
The Encov team at Institut Pascal, Clermont-Ferrand, France is looking for a postdoc student to work on deformable 3D reconstruction ambiguities (Link to offer). The goal of this project is to uncover the disambiguates in deformable 3D reconstruction in order to self-calibrate the camera.
The postdoc will be supervised by Prof. Adrien Bartoli ([email protected]) and Shaifali Parashar ([email protected]) .
Requirements: Strong background in computer vision and mathematics with a strong publishing record Strong programming skills in C++ and python
Fluency in English
Project duration: 12 months (with a possible extension)
Tentative start date: September 2022
How to apply: Please send your CV and list of publications to Prof. Adrien Bartoli and Shaifali Parashar with the subject "Postdoc position in Deformable 3D Reconstruction Ambiguities".
location: Conservatoire National des Arts et Métiers, CEDRIC Laboratory, Paris, France
Context: This postdoctoral position takes place within the industrial projet ARC.
The aim of this project is to provide automatic solutions that will improve cyber incident response which is a key part in the domain of information system security.
It is a critical process, operated by SOC experts.
The latter, although currently having a panoply of tools, must mobilize, under time pressure, a wide range of knowledge and skills allowing them to react, in a very short time, to an attack to neutralize its harmful effects as soon as possible and therefore to reduce its extent and costs.
The ARC project aims, among other objectives, to implement a contextualized decision support system based on an ontology that capitalizes on all the knowledge useful to human reasoning.
The position responsibilities include: - Investigating existing sources that contribute to cyber incident response decision-making
- conceptualizing an ontology that could be used for a contextualized response to an incident
- Operationalizing the conceptualized ontology.
Requirements: The ideal candidate holds a doctoral degree in Computer science or a related field and is able to combine theoretical and practical aspects in his/her work. Fluent English communication and software technology skills are fundamental requirements. The candidate should have a background in at least one of the following fields:
- Semantic web technologies
- Knowledge representation
- Logic, automated reasoning, or related subfields
It would be a plus if the candidate has a background in cyber security field.
Full-stack developing skills are required.
Duration: Seven months, renewable, starting as soon as possible.
Contact: Nadira Lammari: [email protected]
We invite applications for a 3-year PhD position at the University of Lille in the context of the recently funded research project "COMANCHE" (Computational Models of Lexical Meaning and Change). The position is funded by Inria, the French national research institute in Computer Science and Applied Mathematics.
COMANCHE proposes to transfer and adapt neural word embeddings algorithms to model the acquisition and evolution of word meaning, by comparing them with linguistic theories on language acquisition and language evolution.
At the intersection between Natural Language Processing, psycholinguistics and historical linguistics, this project intends to validate or revise some of these theories, while also developing computational models that are less data hungry and computationally intensive as they exploit new inductive biases inspired by these disciplines.
The first strand of the project, on which the successful candidate will work, focuses on the development of computational models of semantic memory and its acquisition. Two main research directions will be pursued.
On the one hand, we will compare the structural properties associated to different semantic spaces derived from word embedding algorithms to those found in human semantic memory as reflected in behavioral data (such as typicality norms) as well as brain imaging data.
The latter data will then used as additional supervision to inject more hierarchical structure into the learned semantic spaces. One the other hand, we intend to experiment with training regimes for word embedding algorithms that are closer to those of humans when they acquire language, controlling the quantity as well as the linguistic complexity of the inputs fed to the learning algorithms through the use of longitudinal and child directed speech corpora (e.g., CHILDES, Colaje). In both cases, both English and French data will be considered.
The successful candidate holds a Master's degree in computational linguistics or computer science or cognitive science and has prior experience in word embedding models. Furthermore, the candidate will provide strong programming skills, expertise in machine learning approaches and is eager to work across languages.
The position is affiliated with the MAGNET team at Inria, Lille [1] as well as with the SCALAB group at University of Lille [2] in an effort to strenghten collaborations between these two groups, and ultimately foster cross-fertilizations between Natural Language Processing and Psycholinguistics.
Applications will be considered until the position is filled. However, you are encouraged to apply early as we shall start processing the applications as and when they are received. Applications, written in English or French, should include a brief cover letter with research interests and vision, a CV (including your contact address, work experience, publications), and contact information for at least 2 referees. Applications (and questions) should be sent to Angèle Brunellière ([email protected]) and Pascal Denis ([email protected]).
The starting date of the position is 1 October 2022 or soon thereafter, for a total of 3 full years.
As part of a project, I am looking for a post-doctoral researcher who has expertise in multimodal interaction, or augmented reality interaction.
The aim of the research project (a collaboration with several Walloon and French companies) is the design of a vehicle control system integrated into an augmented display helmet, using speech, gaze, user attention level, and possibly other modalities.
At the level of the identified research issues on which Namur will work, beyond the definition of a natural and effective multimodal dialogue, as well as certain software aspects, there will be questions related to the adaptation of the multimodal interface.
A few small remarks: the position is a full-time position over 2 years, with a competitive salary. The exact job title is "Research Fellow with Thesis".
The position would ideally start on September 1, but the actual start date may be a little delayed. Applications must be submitted by 15 July.
More info + application on the following page: https://jobs.unamur.be/ emploi.2022-05-18.6143812019
The Department of Earth, Environmental and Resource Sciences at the University of Texas El Paso (UTEP), invites applications for a postdoctoral position on Machine Leaning applications to Computational Fluid Dynamics and Hydrologic Modeling.
We seek a PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Earth Science, Environmental Science, Environmental Engineering, Civil Engineering, Geography, Water Resources Engineering, Fluvial Geomorphology or a related physical science field.
The appointment is for 1-year initially but potentially renewable. The Postdoctoral Research Associate will work under the supervision of Professors Laura V. Alvarez and Hernan A. Moreno within the newly created center for Geospatial Sensing and Sampling of the Environment (GeoSenSE).
The guiding research topic is on supervised, unsupervised and reinforced learning to improve Computational Fluid Dynamics (CFD) models that study macro-turbulence, sediment transport and bed evolution in large-scale river systems.
The desired skills of potential candidates are: (1) Expertise on both machine and deep learning techniques, and (2) Computer programming using Linux platforms (e.g. c++, R or Python). Applications will be reviewed in a daily basis. Interested candidates should email their CV to Prof. Alvarez ([email protected]) and prof. Moreno ([email protected]). Applications will be reviewed until a suitable candidate is found.
We are hiring pre/post-doctoral candidates at TCS Research in our Visual Computing group.
The aim of the researchers will be to publish papers in top-tier computer vision and graphics conferences.
Some Sample Projects 1.3D virtual try-on: Aim is to build a system to drape a 3D garment on a 3D human body and animate it according to the change in human poses.
2. 3D Digital human: In this project, we will primarily focus on the accurate estimation of 3D human body shape, pose and a high-quality expressive face estimation from video.
3. 3D Surface Reconstruction: Creating 3D mesh of an environment from a video for re-rendered reality
4. Inverse Rendering for Physics Aware Augmented Reality : In this work, we aim to jointly estimate the albedo, normals, depth and 3D spatially-varying lighting from a set of images.
Who can apply?: Pre-doc : Recent CSE/ECE MTech/MS Graduates/ or graduating in next few months.
Post-doc: Recent Computer Science Ph.D.’s or candidates who have submitted their thesis or very near to it.
We are looking for candidates with the following skills:
Domain Skills: Computer Vision: 3D Reconstruction, Stereo Vision, Epipolar Geometry. Computer Graphics: Basic understanding of 3D meshes, operations over 3D meshes, and rendering.
Deep Learning: Hands-on experience in training deep neural networks like ResNet’s, GAN’s, knowledge of graph neural networks would be a plus.
Technical Skills: Coding Language: Python (Primary), C++ (Basic), OpenGL Deep Learning Framework: PyTorch (Primary), Tensorflow (Basic)
Good interpersonal skills to communicate ideas, experimental results, and analysis with other team members.
Interested candidates can send their CV with the information that shows the required domain and technical skills. Send CVs to [email protected] with the subject line “Research Opportunities, TCS Research”.
At the end of the pre-doc/post-doc, the candidates can explore a full-time offer from TCS Research based on their interests and performance.
URGENT from the beginning of the school year on September 1, 2022 until August 31, 2023, we are looking for a full-time computer science teacher.
Job description: https://espaces-collaboratifs. grenet.fr/share/s/ 7B8XsOUySt6Vi3cOJ-LMTg
Application: Monday 4 July evening.
We are currently looking for a candidate for a doctoral thesis in computer science, on a collaborative project likely to be funded by the DGA. The thesis will take place within the R2I team (Research of Information and Interactions) of the Data Sciences pole of the LIS (Marseille).
The details of the subject can be found below, or on the link: https://adrianchifu.com/sujet-de-these-de-doctorat/ .
Applications and requests for information must be sent no later than July 15 to [email protected] and [email protected]
With our apologies for any multiple mailings, we wish you a good day.
Best regards, Patrice BELLOT and Adrian CHIFU
Doctoral thesis topic Title : Automatic generation of fluent summaries of texts in French by deep learning
Supervision : Prof. Patrice BELLOT ( https://cv.archives-ouvertes.fr/patrice-bellot ; University of Aix-Marseille CNRS, LIS), Adrian CHIFU ( https://adrianchifu.com ; University of Aix-Marseille CNRS, LIS )
Period: October 2022 - September 2025
Keywords: automatic summarization, text thinning, information retrieval, natural language processing, machine learning, neural networks
Context : Collaborative project likely to be supported by the DGA between : QWAM ( https://www.qwamci.com );
the ISIR MLIA team ( https://www.isir.upmc.fr/equipes/mlia/presentation/ ) the R2I team of the LIS ( https://www.lis-lab.fr/r2i/ ) : the thesis will take place within the R2I team (Research of Information and Interactions) of the Data Sciences pole of the LIS
Subject description:
The context of the project Faced with the exponential growth in volumes of data and particularly in text-type documentation (manuals, publications, websites, etc.), one solution is to provide easy access to essential elements, through summaries of most relevant texts in the user context . However , to date, automatic summaries remain perfectible, both from the point of view of information coverage and their susceptibility to creating false information or even their fluidity of reading , a criterion which is the primary target of this thesis .
The aim of the RAFFAL project is to improve automatic technologies (by AI) of document summaries in French according to the angle of the metrics that govern them as an objective function (automatic learning of models) and a measure of human evaluation . In addition , next-generation algorithms, models and datasets based on the most recent deep learning technologies (in particular Transformer type and sequence-to-sequence models ) are almost exclusively in the English language and must be tested and adapted to the French .
The field of automatic summarization has long been confronted with the lack of sufficiently reliable metrics for automatically evaluating the quality of the summaries provided ; this lack of evaluation metrics is a major obstacle to the industrialization and deployment of automatic summary technologies for which trust and management criteria are essential.
Workplan The work plan has two major components. The first corresponds to a study of the properties and limits of existing metrics and their adaptation to French. The second corresponds to the modification of the objective functions used for the training of the models according to the adapted metrics and new metrics.
The thesis that we propose will first of all attack the definition of fluidity . Existing measures of fluidity and quality of a summary , generally for English, will be studied and adapted to the French language. This involves, for example, revisiting the link between existing measures, the different qualitative dimensions of a summary and their implementation within a neural architecture, particularly of the sequence-to-sequence type (depth of representations and levels of abstraction, attentional mechanisms). Linguistic resources and useful text corpora should be identified.
Human evaluators may be involved and we must both study measures of agreement inter-annotators and analyze their profiles , according to their level of knowledge the topic of the abstract, for example. An online evaluation could make it possible to identify the points complicating reading and lead to new metrics which will in turn influence the dynamic creation of a summary (reinforcement approach, alternative rewriting, informational completion by information extraction or annotation semantics).
Fluidity will be studied as an objective function for the optimization of the "compromise" between information loss and hallucination phenomena (collaboration with another thesis carried out in parallel within the ISIR laboratory of Paris Sorbonne University) . We will study the balance between fluency, on the one hand, and informational quality and completeness, on the other hand (e.g. the "trade-off" between precision and recall, for the results of a research). This phase will require the identification of the essential information, the central textual elements of the texts to be summarized and can be approached by means of question-answer systems.
Finally, the fluidity of a summary being context -dependent , it is necessary to study its character subject i f , in particular taking into account the types of text (news, position papers, interviews with dialogues, scientific articles, etc. ) and the priorities of the summary (coverage of points of view and opinions on a subject without losing the identification of sources, factual synthesis around an event, etc. .
Each step will be the subject of experiments on real data and problems, in collaboration with the project's industrial partner. The thesis proposals will fall within the framework of open science (publications, data and models when possible, source codes).
Candidate profile: Previous background: Master 2 in Computer Science oriented Research in IA or TAL or equivalent
Language: French (minimum level C1)
Programming language : Python
Desired knowledge and skills :
- statistical machine learning, neural architectures, transformers
- automatic document classification
- corpus annotation
- Natural Language Processing tools and resources
- language models and textual representations - automatic summarization, text generation, text simplification
- research of information and questions-answers
I inform you of the opening of a permanent position (CIRAD, UMR TETIS) entitled " Chercheur.se in Data Science and Modeling".
Context : CIRAD (Centre for International Cooperation in Agronomic Research for Development) produces and transmits new knowledge to support innovation and agricultural development in developing countries with its partners. Its priority objective is to build sustainable agriculture in tropical and Mediterranean regions, adapted to climate change, capable of feeding 10 billion human beings in 2050, while preserving the environment.
Find out more about CIRAD: www.cirad.fr
== Position/assignment description The TETIS unit (Territories, environment, remote sensing and spatial information) is looking for a Chercheur.se in Data science and modelling. The position aims to improve monitoring and alert systems by responding to societal challenges in health and food safety.
- Animal health monitoring, and in particular the early detection of the emergence of pathogens worldwide, is one of the means of preventing or anticipating the introduction of health hazards, particularly in a One Health context. The aim of the monitoring systems developed at TETIS in collaboration with other units (in particular ASTRE) is to have reactive tools, complementary to official sources.
- Food security monitoring systems represent another risk prevention or anticipation challenge, particularly in the South. Indeed, agricultural risks are all the more acute in West Africa as the national surveillance and monitoring services may be lacking due to a lack of technical and financial means. Although agro-climatic data have been widely used for this purpose, the use of other data sources (household surveys, social media, press, market analysis, price monitoring) can prove to be complementary or even essential for the systems. alert.
In collaboration with researchers in data science and modeling from UMR TETIS, you will design and implement original methods crossing data-oriented approaches (Data science, machine learning, etc.) and process-oriented (spatial process modeling and dynamics in interaction at different scales in the territories).
In a multidisciplinary framework based on the two fields of application, you will make generic proposals for linking based on the extraction, selection and exploitation of thematic, spatial and temporal descriptors from the different data sources. This will be based on the mobilization of Data Science and machine learning methods that can be guided by expert knowledge and existing models. Finally, particular attention will be paid to producing methods with a form of “semantization” and explainability of the results that are essential in a multidisciplinary framework and interaction with experts.
== Desired profile With a scientific background, you hold a doctorate in computer science. The expected skills are: - In-depth knowledge of data science and/or machine learning with knowledge of process-oriented approaches (modeling). - Experience in extracting and combining heterogeneous multi-source data is desired.
- Major publications in data science. - Good ability to work in a team and in a partnership network. - Concern for the operational transfer of research, interest in training and in interactions with actors/partners. - Taste for multidisciplinarity.
- Autonomy and leadership. - Good command of English reading, writing and speaking. - An experience of 1 year or more after thesis is desirable. - International research experience would be a plus.
== Position location - Montpellier, with possibilities of assignment to the South after 2 years
Start date: 01/11/2022
== Job Information Matthew Roche [email protected]
Reference: P-ES-TETIS-2022-06-CDI-6412
To apply: https://recrutement.cirad.fr/offre-de-emploi/emploi-chercheur-se-en-science-des-donnees-et-modelisation_6412.aspx
Applications before 08/28/2022
Environment The National Research Institute for Agriculture, Food and the Environment (INRAE) is a public research establishment placed under the dual supervision of the ministry in charge of agriculture and the ministry in charge of research.
In order to provide the scientific, academic and private communities with renewed and efficient technological capacities, it is proposed to formalize a national Consumer/Food/Health research infrastructure (CALIS[1]) from three poles, built on systems recognized collectives (labelled) for the production, processing and management of data relating to consumer behavior, the construction of food quality and their characterization, and the characterization of their nutritional properties and their impacts on health in the 'Man.
In this context, it is essential to allow the CALIS infrastructure to provide data sources that respect the FAIR[2] principles, principles that are now becoming an essential criterion for evaluating data quality.
The objective of the T-CALIS-FAIR project is to propose an IS architecture prototype at the scale of the TRANSFORM Department and its partners within the framework of the CALIS research infrastructure leading to the creation, management and exploitation of databases relating to the processes studied and the properties of the products resulting therefrom. Beyond CALIS, the ambition is to design an IS architecture that is potentially generalizable to all areas: food, biobased products and residues.
Mission and activities Attached to the IATE[3] unit of the TRANSFORM[4] department, you will work in collaboration with the members of the knowledge engineering team of the unit[5] on the T-CALIS-FAIR project. You will be in charge of capturing the needs expressed by users of the various Collective Scientific Infrastructures (ISC Planet, ISC Lait STLO, and ISC URTAL), prototyping and evaluating the various data integration technologies, and design of the development and qualification of the deployed solution.
Skills and know-how You must have one or more skills and soft skills from the list below.
* Java developments (Spring, Spring boot) * Development of webservices, API Rest, API Soap
* Best practices and tools for quality control, version control, test and release methodologies (Git, Sonar, …) * In-depth knowledge of a specification and design method (UML, Design patterns) * Knowledge of a RDBMS (PostGreSQL, Mysql, MongoDB, …)
* Know how to work in project mode, autonomy in the respect of the objectives set, * Capacity for organization and dialogue, rigour, spirit of method, * Taste for teamwork, ability to dialogue with functional teams, sense of responsibility and commitment.
* Define the technical clauses of a specification * Ensure the deployment of the application (installation, assistance, training, evaluation) * Maintain the application (diagnose faults, correct them), and make it evolve * Evaluate the workload and costs of software development
The following skills are additional assets: * Knowledge of Docker * Knowledge of semantic web languages (OWL, RDF, SPARQL)
Ability to adapt and autonomy Good animation and dialogue skills Initiative
Level of training and experience required
Minimum level Bac + 4, Master's degree in computer science
Location The position is located in Montpellier Occasional trips to Paris as well as to other INRAE locations throughout the national territory are to be expected.
Name: Patrice Buche Email: [email protected]
Name: Julien Cufi Email: [email protected]
Name: Pierre Bisquert Email: [email protected]
As part of the MALIN project (MAnuels scoLaires INclusifs) https://anr.fr/Projet-ANR-21-CE38-0014
the CEDRIC laboratory of the CNAM in Paris and the Interdisciplinary Laboratory of Digital Sciences (LISN) in Orsay offer thesis funding in Automatic Language Processing and Artificial Intelligence.
The topic focuses on the extraction and characterization of multimedia content, applied to the case of textbooks.
The details of the offer are attached.
To apply, please send a CV, M1 and M2 grades and a cover letter to Camille Guinaudeau ([email protected]), Olivier Pons ([email protected]) and Caroline Huron ([email protected]).
Please share this information in your networks and to anyone who may be interested.
Kind regards Camille Guinaudeau, Olivier Pons and Caroline Huron
We are Sightengine (https://sightengine.com), a French start-up specialized in the analysis and automated moderation of content.
We develop our own content analysis solutions and make them available via simple and public APIs.
These are used by major players such as social networks, e-commerce, media.
We are looking for *two profiles* to work in *artificial intelligence applied to NLP* (https://sightengine.com/careers):
- NLP Data Scientist - You will work on automated solutions for understanding and analyzing texts in multiple languages: state of the art, implementation of proofs of concept, model training, large-scale deployment - Data scientist Audio & Language
- You will work on the analysis, classification and transcription of soundtracks, as well as on the analysis of non-verbal elements: implementation of innovative solutions , model training and deployment
These are full-time permanent positions, remotely, with a function as soon as possible.
You can submit your application and see more details here: https://sightengine.com/careers