Dear colleagues, SIGMA Clermont (France) invites applications for two postdoctoral positions to contribute to the fields of multi-modal perception and control, applied to collaborative assembly tasks.
Within an European project, the selected candidates will be in charge of the multi-modal perception component of the common architecture that will identify the state of the product, the environment and the collaborating human during the execution of a production task.
The details of this position can be found in the following link: https://owncloud.sigma-clermont.fr/index.php/s/wwkT7MeS80lCP8p
Eligibility: Candidates must have: (i) a doctoral degree in robotics, computer science, or a closely related field; (ii) a strong record of peer-reviewed publications e.g., IROS, ICRA, TRO, RSS, ECCV/ICCV, CVPR; (iii) good skills in programming and use of related libraries such as C++/Python, ROS, Pytorch, TensorFlow, etc.
How to apply: Applicants must submit a curriculum vitae including the contact information of two professional references in a single PDF file to [email protected], [email protected], [email protected]. Email subject should be [ACROBA_Postdoc]
Start date: ASAP.
Net salary: ~ 2100 €/month.
About SIGMA: SIGMA Clermont trains top engineers in different areas of engineering sciences (chemistry, advanced mechanics, industrial engineering, robotics) in a multicultural environment. SIGMA Clermont is an internationally oriented school, driven by research and strongly connected to the world of enterprise. It is located in the region Auvergne-Rhone-Alpes, the first region in France in terms of industrial employment, and the eighth-richest region in Europe. It hosts numerous large international groups, medium-sized sector leaders, and innovative start-ups. Clermont-Ferrand and its vicinity have a population of about 290,000 residents and are ranked among the most livable small cities in the country. One of the most famous volcanoes area (Chaîne des Puys) awarded World Heritage status by Unesco is situated at only 10 kilometers from center town.
Aly Magassouba, Assistant Professor Institut Pascal UMR 6602 UCA/CNRS/SIGMA
Emmanuel CARTIER [email protected]
*Postdoctoral researcher in Computational Diachronic Semantics*
Labex EFL (Empirical Foundations of Linguistics, Paris, https://en.labex-efl.fr/)
Strand 5: Computational Semantic Analysis
Research area: interpretable computational models for automatic detection and monitoring of semantic evolutions: combination of Contextual Embeddings and Pattern Mining approaches
Contract duration: 18 months
Location: Paris
Research Laboratory: Sorbonne Paris Nord University, LIPN UMR7030 CNRS
Application deadline: November 15, 2021
Audition period: November 15-30, 2021
Job Starting date: from January 1, 2022
Context, Issues and research axes Languages are constantly evolving, driven by the need to adapt to socio-cultural and technological developments and to make communication more efficient and expressive. In particular, new words are forged or borrowed from other languages, some words become obsolete, others acquire new meanings or lose existing meanings.
In NLP, the study of language dynamics, especially from the lexical point of view, has gained audience in recent years, complementing synchronic approaches. The field of research is structuring itself, with recent state of the art (Monteirol et al., 2021; Tahmasebi et al., 2021) and several scientific events (International Workshop on Computational Approaches to Historical Language Change 2019 and 2021, ACL 2019 and 2020). Two initial evaluation tasks have been proposed (Unsupervised Lexical Semantic Change Detection Task, SemEval2020) and reference sets have been set up for four languages (English, Latin, Swedish and German).
Lexical change detection systems have followed advances in NLP methods: after the first systems essentially based on frequency changes (for example Gulordova & Baroni, 2011), systems used word embeddings (Kim et al., 2014, Schletchweg et al., 2019) and more recently contextual embeddings (Hu et al., 2019; Martinc et al., 2019; Giulianelli et al., 2020). These latter systems generally proceed by grouping the contextual vector representations of the different uses into clusters of meaning, then detect changes according to different metrics (Monteirol et al. 2021). Current systems still face many limitations.
Mainly, the opacity of neural models does not make it possible to characterize these evolutions, in particular it is difficult, if not impossible, to link the semantic changes to linguistic morphological, syntactic or lexico-syntactic features, or to categorize the types of changes (extension, restriction, metaphor, metonymy, etc.).
To this end, one avenue would be to combine neural approaches with Pattern Mining (Béchet et al. 2015) or collocation extraction approaches from corpus linguistics (for example Gries, 2012) which make it possible to extract the most salient lexico-syntactic patterns of a given meaning from a corpus of occurrences and thus identify the evolution. It would also be interesting to use the contextual information of the occurrences (date, type of source, domain, diatopic and diastratic features, etc.) to characterize and follow the evolution of usages.
The job main objective is therefore to set up a system combining these approaches to allow an automatic characterization of semantic evolutions. The first step will consist in experimenting with state-of-the-art models for detecting changes.
The second step will then try to combine contextual embeddings and pattern mining approaches / collocation extraction to highlight the linguistic characteristics of each of the meaning clusters and their evolution.
The studied corpora will be mainly in English and French. The postdoctoral fellow will work in collaboration with computer scientists and linguists from the Labex who are currently building a reference corpus of semantic evolutions for French (following the Durel methodology: Schlechtweg et al., 2018).
Other issues may also be addressed by the recruited person, and in particular: current systems do not take into account the graduality of evolutions, generally being limited to comparing two synchronic language states; to get the vector representation of a lexis in a context, it is possible to use one of the hidden layers or a combination of them.
There is currently no consensus on the most adequate layer to take into account to obtain the most adequate semantic representation.
The recruited person will join the strand 5 (“Computational Semantics”) of the Labex, specifically the research team working on the “Semantic Variation and Change” operation which aims to:
- develop new models and methods for the automatic detection of lexical semantic changes, the typology of changes from intra- and extra-linguistic points of view;
- develop a reference dataset of semantic evolutions in contemporary French, based on available diachronic corpora.
Candidate profile - PhD in computer science specialised in Computational Linguistics and Machine Learning
- deep learning methods and language models attested training and experience
- working language: French and / or English
Application
Please send :
- a cover letter
- a description of the research project related to the research questions
- a CV with a list of publications and 3 representative publications (pdf or link),
- letters of recommendation or names of two referees.
before November 15, 2021. The auditions of the pre-selected candidates will take place at the end of November 2021.
François Portet [email protected]
Call for post-doc applications in Natural Language Processing for scientometrics (Grenoble Alps University, France)
Starting date: January. 03, 2022 at the earliest
Duration: full-time position for 24 months (with a possibility of reappointment)
Salary: according to experience (up to 4142€/ month)
Application deadline: 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
*Context* 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 (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) techniques.
*Main tasks* 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 on-line 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 the content of both cited and citing documents, whether the citation occurs in a scientific paper, a tweet or an on-line 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 post-doc would also be expected to lead the diffusion of corpus collected through open source platforms and/or open shared tasks organisation.
*Scientific environment* 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.
*Requirements* 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 protected]), François Portet ([email protected]), Frédérique Bordignon ([email protected]).
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
Thank you to broadcast this announcement to potential applicants (Ph.D graduates seeking a Post-doc).
One post-doc opening that needs to be filled as soon as possible is available at the Laboratory of Informatics Image Interaction (L3i -- http://l3i.univ-larochelle.fr) of La Rochelle University (http://www.univ-larochelle.fr/?lang=en), located in the magnificent city of La Rochelle by the West (Atlantic) Coast of France (2h25 to 2h45 from Paris by high speed train).
The position targets "use of process mining to design a dashboard for tracking the learning of the highway code" and is in the frame of a collaborative project on Learning Analytics.
The successful applicant is expected to join the Lab. for 1-year contract, starting as soon as possible.
Applicants should e-mail their resume with a list of two references to Dr. Ronan CHAMPAGNAT ([email protected]), with [Post-doc Application] as a prefix of the email subject. Applications will be accepted until this position is filled.
For more information, do not hesitate to send an email as well. I’ll will be glad to provide more details.
Kind regards, Ronan CHAMPAGNAT
-- Dr Ronan CHAMPAGNAT - IUT de La Rochelle -- IUT : 0546513927 ; L3i : 0546457204 ; mobile : 0663969956 -- http://pageperso.univ-lr.fr/ronan.champagnat
Cyber-Physical Systems
The Modeling and Verification team at Institut de Recherche en Informatique Fondamentale IRIF (CNRS and Universit ́e de Paris) is looking for a motivated student to begin an internship on the subject of learning-based formalization of cyber-physical systems.
The internship can be followed with a PhD thesis depending on the candidate’s progress.
1 Context and motivation Cyber-physical systems (CPS) are computer-based systems where the computer software and the physical world (of both the system and its environment) are tightly coupled in a possibly distributed setting. CPS are omnipresent in everyday’s life and their applications continue to proliferate — a notable example is robotics and collaborative autonomous systems [7, 8].
One major challenge of CPS research is verification and validation, i.e. how to bring together the knowledge from the engineering and computer science communities in order to build safe and secure CPS, or rigorously verify existing CPS [5, 2].
For existing CPS, formalization, i.e. developing a formal model that faithfully represents the underlying CPS on which formal verification activities may be carried out, is a necessary step.
Formalization of CPS is particularly difficult and costly due to the complexity of CPS and the absence of formal semantics that precisely describe their behavior. Recent works tackling the formalization problem in the context of robotics underlined the difficulties around the formalization activity even in the presence of automated transformations [4, 3].
In many cases, formalization is unfeasible using traditional “mapping” techniques, because the underlying CPS has a partially described model, or no model at all (black-box CPS). To “formalize” blackbox CPS, learning techniques can be useful.
For instance, there exists a solid body of research on learning hybrid automata and their subclasses, e.g. timed automata, a pioneer theory in formal verification [9, 1, 10].
The goal of this internship is to study the possibilities of deriving formal (hybrid automata) models out of black-box CPS using learning techniques on the CPS inputs and outputs.
Depending on the candidate’s performance, a PhD thesis will follow with two objectives.
First, the learning process must lead to hybrid automata models in which a sufficient confidence is reached so they can replace the black- box of the CPS.
Second, the obtained hybrid automata models will be extended with Fault Detection, Isolation and Recovery (FDIR) components in order to formally verify important properties at runtime and adequately react to their possible violation (in the style of [3, 6]).
The theories developed within the internship/thesis will be applied to case studies featuring autonomous robots.
2 Candidate, duration and location The intern will be hosted at IRIF, a research institue of CNRS and Universit ́e de Paris. The research conducted at the institute is based on the study and understanding of the foundations of all computer science, in order to provide innovative solutions to the current and future challenges of digital sciences.
In particular, IRIF is renown for its contributions to the design and analysis of algorithms, the study of computational and data representation models, the foundations of programming languages, software development, verification, and certification.
IRIF also conducts interdisciplinary research taking advantage of its scientific approach.
The preferable start date is February-March 2022, but arrangements are possible.
Priority will be given to about-to-graduate students (second year of masters or equivalent), but students with planned graduation in 2023 (first year of masters or equivalent) may also apply. The internship will last 5 to 6 months.
If the intern is expected to graduate in 2022, the modeling and verification team may offer them a three-year PhD (depending on their progress and performance during the internship) funded by Franco-Japanese CyPhAI project.
In this case, the PhD candidate will be hosted at IRIF with possible research visits to Verimag, Universit ́e Grenoble Alpes (France) and/or Kyoto University (Japan).
For any questions regarding this position or to send your application, please email the intern- ship’s/PhD advisors :
Prof. Eugene Asarin ([email protected]) Assoc. prof. Mo Foughali ([email protected])