Hi there,
Could you please distribute the following job offer? Thanks.
Best,
Pascal
-------------------------------------------------------------------------------------
We invite applications for a 3-year PhD position co-funded by Inria,
the French national research institute in Computer Science and Applied
Mathematics, and LexisNexis France, leader of legal information in
France and subsidiary of the RELX Group.
The overall objective of this project is to develop an automated
system for detecting argumentation structures in French legal
decisions, using recent machine learning-based approaches (i.e. deep
learning approaches). In the general case, these structures take the
form of a directed labeled graph, whose nodes are the elements of the
text (propositions or groups of propositions, not necessarily
contiguous) which serve as components of the argument, and edges are
relations that signal the argumentative connection between them (e.g.,
support, offensive). By revealing the argumentation structure behind
legal decisions, such a system will provide a crucial milestone
towards their detailed understanding, their use by legal
professionals, and above all contributes to greater transparency of
justice.
The main challenges and milestones of this project start with the
creation and release of a large-scale dataset of French legal
decisions annotated with argumentation structures. To minimize the
manual annotation effort, we will resort to semi-supervised and
transfer learning techniques to leverage existing argument mining
corpora, such as the European Court of Human Rights (ECHR) corpus, as
well as annotations already started by LexisNexis. Another promising
research direction, which is likely to improve over state-of-the-art
approaches, is to better model the dependencies between the different
sub-tasks (argument span detection, argument typing, etc.) instead of
learning these tasks independently. A third research avenue is to find
innovative ways to inject the domain knowledge (in particular the rich
legal ontology developed by LexisNexis) to enrich enrich the
representations used in these models. Finally, we would like to take
advantage of other discourse structures, such as coreference and
rhetorical relations, conceived as auxiliary tasks in a multi-tasking
architecture.
The successful candidate holds a Master's degree in computational
linguistics, natural language processing, machine learning, ideally
with prior experience in legal document processing and discourse
processing. Furthermore, the candidate will provide strong programming
skills, expertise in machine learning approaches and is eager to work
at the interplay between academia and industry.
The position is affiliated with the MAGNET [1], a research group at
Inria, Lille, which has expertise in Machine Learning and Natural
Language Processing, in particular Discourse Processing. The PhD
student will also work in close collaboration with the R&D team at
LexisNexis France, who will provide their expertise in the legal
domain and the data they have collected.
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 Pascal Denis
(pascal.denis(a)inria.fr).
The starting date of the position is 1 November 2022 or soon
thereafter, for a total of 3 full years.
Best regards,
Pascal Denis
[1] https://team.inria.fr/magnet/
[2] https://www.lexisnexis.fr/
--
Pascal
----
Pour une évaluation indépendante, transparente et rigoureuse !
Je soutiens la Commission d'Évaluation de l'Inria.
----
+++++++++++++++++++++++++++++++++++++++++++++++
Pascal Denis
Equipe MAGNET, INRIA Lille Nord Europe
Bâtiment B, Avenue Heloïse
Parc scientifique de la Haute Borne
59650 Villeneuve d'Ascq
Tel: ++33 3 59 35 87 24
Url: http://researchers.lille.inria.fr/~pdenis/
+++++++++++++++++++++++++++++++++++++++++++++++
Dear colleagues,
Last month, we shared the result of our collaborative work on a core metadata scheme for learner corpora with LCR2022 participants. Our proposal builds on Granger and Paquot (2017)'s first attempt to design such a scheme and during our presentation, we explained the rationale for expanding on the initial proposal and discussed selected aspects of the revised scheme.
Our proposal is available at https://docs.google.com/spreadsheets/d/1-RbX5iUCUtCBkZU9Rfk-kv-Vzc--F-eUW2O…<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>
We firmly believe that our efforts to develop a core metadata scheme for learner corpora will only be successful to the extent that (1) the LCR community is given the opportunity to engage with our work in various ways (provide feedback on the general structure of the scheme, the list of variables that we identified as core and their operationalization; test the metadata on other learner corpora; use the scheme to start a new corpus compilation, etc.) and (2) the core metadata scheme is the result of truly collaborative work.
As mentioned at LCR2022, we will be collecting feedback on the metadata scheme until the end of October. The online feedback form is available at:
https://docs.google.com/document/d/1NeDUuxGJlPSJI9wHVA1xgGM-aV8jXTa8Qlb45K-…<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>
We'd like to thank all the colleagues who already got back to us (at LCR2022, by email or via the online form). We also thank them for their appreciation and enthusiasm for our work! We'd also like to encourage more colleagues (and particularly those of you who have experience in learner corpus compilation) to provide feedback! We need help in finalizing the core metadata scheme to make sure that it can be applied in all learner compilation contexts. In short, we need you to make sure the scheme meets the needs of the LCR community at large.
With very best wishes,
Magali Paquot (also on behalf of Alexander König, Jennifer-Carmen Frey, and Egon W. Stemle)
Reference
Granger, S. & M. Paquot (2017). Towards standardization of metadata for L2 corpora. Invited talk at the CLARIN workshop on Interoperability of Second Language Resources and Tools, 6-8 December 2017, University of Gothenburg, Sweden.
Dr. Magali Paquot
Centre for English Corpus Linguistics
Institut Langage et Communication
UCLouvain
https://perso.uclouvain.be/magali.paquot/
Dear all
Just wanted to let you know that APJCR Vol. 3, No. 1 is now available to
view online.
http://icr.or.kr/ejournals-apjcr
CK
---
*CK Jung BEng(Hons) Birmingham MSc Warwick EdD Warwick Cert Oxford*
Department of English Language and Literature, Incheon National
University, *South
Korea*
Vice President | The Korea Association of Primary English Education
(KAPEE), *South Korea*
Vice President | The Korea Association of Secondary English Education
(KASEE), *South Korea*
Director | Institute for Corpus Research, Incheon National University, *South
Korea* (http://icr.or.kr)
Editor | Asia Pacific Journal of Corpus Research, ICR, *International* (
http://icr.or.kr/apjcr)
Deputy Editor | Korean Journal of English Language and Linguistics,
KASELL, *South
Korea*
Editorial Board | Corpora, Edinburgh University Press, *UK*
Editorial Board | English Today, Cambridge University Press, *UK*
E: ckjung(a)inu.ac.kr / T: +82 (0)32 835 8129
H(EN): http://ckjung.org
H(KR): http://prof1.inu.ac.kr/user/ckjung
PhD in ML/NLP – Efficient, Fair, robust and knowledge informed
self-supervised learning for speech processing
Starting date: November 1st, 2022 (flexible)
Application deadline: September 5th, 2022
Interviews (tentative): September 19th, 2022
Salary: ~2000€ gross/month (social security included)
Mission: research oriented (teaching possible but not mandatory)
*Keywords:*speech processing, natural language processing,
self-supervised learning, knowledge informed learning, Robustness, fairness
*CONTEXT*
The ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive
and Innovative Speech Technologies) will start on November 1st 2022.
Self-supervised learning (SSL) has recently emerged as one of the most
promising artificial intelligence (AI) methods as it becomes now
feasible to take advantage of the colossal amounts of existing unlabeled
data to significantly improve the performances of various speech
processing tasks.
*PROJECT OBJECTIVES*
Recent SSL models for speech such as HuBERT or wav2vec 2.0 have shown an
impressive impact on downstream tasks performance. This is mainly due to
their ability to benefit from a large amount of data at the cost of a
tremendous carbon footprint rather than improving the efficiency of the
learning. Another question related to SSL models is their unpredictable
results once applied to realistic scenarios which exhibit their lack of
robustness. Furthermore, as for any pre-trained models applied in
society, it isimportant to be able to measure the bias of such models
since they can augment social unfairness.
The goals of this PhD position are threefold:
- to design new evaluation metrics for SSL of speech models ;
- to develop knowledge-driven SSL algorithms ;
- to propose methods for learning robust and unbiased representations.
SSL models are evaluated with downstream task-dependent metrics e.g.,
word error rate for speech recognition. This couple the evaluation of
the universality of SSL representations to a potentially biased and
costly fine-tuning that also hides the efficiencyinformation related to
the pre-training cost. In practice, we will seek to measure the training
efficiency as the ratio between the amount of data, computation and
memory needed to observe a certain gain in terms of performance on a
metric of interest i.e.,downstream dependent or not. The first step will
be to document standard markers that can be used as robust measurements
to assess these values robustly at training time. Potential candidates
are, for instance, floating point operations for computational
intensity, number of neural parameters coupled with precision for
storage, online measurement of memory consumption for training and
cumulative input sequence length for data.
Most state-of-the-art SSL models for speech rely onmasked prediction
e.g. HuBERT and WavLM, or contrastive losses e.g. wav2vec 2.0. Such
prevalence in the literature is mostly linked to the size, amount of
data and computational resources injected by thecompany producing these
models. In fact, vanilla masking approaches and contrastive losses may
be identified as uninformed solutions as they do not benefit from
in-domain expertise. For instance, it has been demonstrated that blindly
masking frames in theinput signal i.e. HuBERT and WavLM results in much
worse downstream performance than applying unsupervised phonetic
boundaries [Yue2021] to generate informed masks. Recently some studies
have demonstrated the superiority of an informed multitask learning
strategy carefully selecting self-supervised pretext-tasks with respect
to a set of downstream tasks, over the vanilla wav2vec 2.0 contrastive
learning loss [Zaiem2022]. In this PhD project, our objective is: 1.
continue to develop knowledge-driven SSL algorithms reaching higher
efficiency ratios and results at the convergence, data consumption and
downstream performance levels; and 2. scale these novel approaches to a
point enabling the comparison with current state-of-the-art systems and
therefore motivating a paradigm change in SSL for the wider speech
community.
Despite remarkable performance on academic benchmarks, SSL powered
technologies e.g. speech and speaker recognition, speech synthesis and
many others may exhibit highly unpredictable results once applied to
realistic scenarios. This can translate into a global accuracy drop due
to a lack of robustness to adversarial acoustic conditions, or biased
and discriminatory behaviors with respect to different pools of end
users. Documenting and facilitating the control of such aspects prior to
the deployment of SSL models into the real-life is necessary for the
industrial market. To evaluate such aspects, within the project, we will
create novel robustness regularization and debasing techniques along two
axes: 1. debasing and regularizing speech representations at the SSL
level; 2. debasing and regularizing downstream-adapted models (e.g.
using a pre-trained model).
To ensure the creation of fair and robust SSL pre-trained models, we
propose to act both at the optimization and data levels following some
of our previous work on adversarial protected attribute disentanglement
and the NLP literature on data sampling and augmentation [Noé2021].
Here, we wish to extend this technique to more complex SSL architectures
and more realistic conditions by increasing the disentanglement
complexity i.e. the sex attribute studied in [Noé2021] is particularly
discriminatory. Then, and to benefit from the expert knowledge induced
by the scope of the task of interest, we will build on a recent
introduction of task-dependent counterfactual equal odds criteria
[Sari2021] to minimize the downstream performance gap observed in
between different individuals of certain protected attributes and to
maximize the overall accuracy. Following this multi-objective
optimization scheme, we will then inject further identified constraints
as inspired by previous NLP work [Zhao2017]. Intuitively, constraints
are injected so the predictions are calibrated towards a desired
distribution i.e. unbiased.
*SKILLS*
*
Master 2 in Natural Language Processing, Speech Processing, computer
science or data science.
*
Good mastering of Python programming and deep learning framework.
*
Previous in Self-Supervised Learning, acoustic modeling or ASR would
be a plus
*
Very good communication skills in English
*
Good command of French would be a plus but is not mandatory
*SCIENTIFIC ENVIRONMENT*
The thesis will be conducted within the Getalp teams of the LIG
laboratory (_https://lig-getalp.imag.fr/_ <https://lig-getalp.imag.fr/>)
and the LIA laboratory (https://lia.univ-avignon.fr/). The GETALP team
and the LIA have a strong expertise and track record in Natural Language
Processing and speech processing. The recruited person will be welcomed
within the teams which offer a stimulating, multinational and pleasant
working environment.
The means to carry out the PhD will be providedboth in terms of missions
in France and abroad and in terms of equipment. The candidate will have
access to the cluster of GPUs of both the LIG and LIA. Furthermore,
access to the National supercomputer Jean-Zay will enable to run large
scale experiments.
The PhD position will be co-supervised by Mickael Rouvier (LIA, Avignon)
and Benjamin Lecouteux and François Portet (Université Grenoble Alpes).
Joint meetings are planned on a regular basis and the student is
expected to spend time in both places. Moreover, the PhD student will
collaborate with several team members involved in the project in
particular the two other PhD candidates who will be recruited and the
partners from LIA, LIG and Dauphine Université PSL, Paris. Furthermore,
the project will involve one of the founders of SpeechBrain, Titouan
Parcollet with whom the candidate will interact closely.
*INSTRUCTIONS FOR APPLYING*
Applications must contain: CV + letter/message of motivation + master
notes + be ready to provide letter(s) of recommendation; and be
addressed to Mickael Rouvier (_mickael.rouvier(a)univ-avignon.fr_
<mailto:mickael.rouvier@univ-avignon.fr>), Benjamin
Lecouteux(benjamin.lecouteux(a)univ-grenoble-alpes.fr) and François Portet
(_francois.Portet(a)imag.fr_ <mailto:francois.Portet@imag.fr>). We
celebrate diversity and are committed to creating an inclusive
environment for all employees.
*REFERENCES:*
[Noé2021] Noé, P.- G., Mohammadamini, M., Matrouf, D., Parcollet, T.,
Nautsch, A. & Bonastre, J.- F. Adversarial Disentanglement of Speaker
Representation for Attribute-Driven Privacy Preservation in Proc.
Interspeech 2021 (2021), 1902–1906.
[Sari2021] Sarı, L., Hasegawa-Johnson, M. & Yoo, C. D. Counterfactually
Fair Automatic Speech Recognition. IEEE/ACM Transactions on Audio,
Speech, and Language Processing 29, 3515–3525 (2021)
[Yue2021] Yue, X. & Li, H. Phonetically Motivated Self-Supervised Speech
Representation Learning in Proc. Interspeech 2021 (2021), 746–750.
[Zaiem2022] Zaiem, S., Parcollet, T. & Essid, S. Pretext Tasks Selection
for Multitask Self-Supervised Speech Representation in AAAI, The 2nd
Workshop on Self-supervised Learning for Audio and Speech Processing,
2023 (2022).
[Zhao2017] Zhao, J., Wang, T., Yatskar, M., Ordonez, V. & Chang, K. - W.
Men Also Like Shopping: Reducing Gender Bias Amplification using
Corpus-level Constraints in Proceedings of the 2017 Conference on
Empirical Methods in Natural Language Processing (2017), 2979–2989.
--
François PORTET
Professeur - Univ Grenoble Alpes
Laboratoire d'Informatique de Grenoble - Équipe GETALP
Bâtiment IMAG - Office 333
700 avenue Centrale
Domaine Universitaire - 38401 St Martin d'Hères
FRANCE
Phone: +33 (0)4 57 42 15 44
Email:francois.portet@imag.fr
www:http://membres-liglab.imag.fr/portet/
*Call for Research Fellow Chairs 2023*
MIAI, the Grenoble Interdisciplinary Institute in Artificial
Intelligence (https://miai.univ-grenoble-alpes.fr/), is opening three
research fellow chairs in AI reserved to persons who have spent most of
their research career outside France (see below). MIAI is one of the
four AI institutes created by the French government and is dedicated to
AI for the human beings and the environment. Research activities in MIAI
aim to cover all aspects of AI and applications of AI with a current
focus on embedded and hardware architectures for AI, learning and
reasoning, perception and interaction, AI & society, AI for health, AI
for environment & energy, and AI for industry 4.0.
These research fellow chairs aim to to address important and ambitious
research problems in AI-related fields and will partly pave the way for
the future research to be conducted in MIAI. Successful candidates will
be appointed by MIAI and will be allocated, for the whole duration of
the chair, a budget of 250k€ covering PhD and/or postdoc salaries,
internships, travels, … They will be part of MIAI and the French network
of AI institutes (comprising, in addition to MIAI, the AI institutes in
Paris, Toulouse and Nice) which provide a very dynamic environment for
conducting research in AI.
*Eligibility*//To be eligible, candidates must hold a PhD from a
non-French university obtained after January 2014 for male applicants
and after 2014-/n/, where /n/ is the number of children, for female
applicants. They must also have spent more than two thirds of their
research career since the beginning of their PhD outside France. Lastly,
they should be pursuing internationally recognized research in
AI-related fields (including applications of AI to any research field).
*To apply* Interested candidates should first contact Eric Gaussier
(eric.gaussier(a)univ-grenoble-alpes.fr) to discuss salary and application
modalities. It is important to note that candidates should identify a
local collaborator working in one of the Grenoble academic research labs
with whom they will interact. If selected, they will join the research
team of this collaborator. They should then send their application to
Manel Boumegoura (manel.boumegoura(a)univ-grenoble-alpes.fr) and Eric
Gaussier (eric.gaussier(a)univ-grenoble-alpes.fr) /by March 11, 2023/.
Each application should comprise a 2-page CV, a complete list of
publications, 2 reference letters, a letter from the local collaborator
indicating the relevance and importance of the proposed project, and a
4-page description of the research project which can target any topic of
AI or applications of AI. It is important to emphasize, in the
description, the ambition, the originality and the potential impact of
the research to be conducted, as well as the collaborations the
candidate has or will develop with Grenoble researchers in order to
achieve her or his research goals.
*Starting date and duration* Each chair is intended for 3 to 4 years,
starting no later than September 2023.
*Location* The work will take place in Grenoble, in the research lab of
the identified collaborator.
For any question, please contact Eric Gaussier
(eric.gaussier(a)univ-grenoble-alpes.fr) or Manel Boumegoura
(manel.boumegoura(a)univ-grenoble-alpes.fr).
*******
(apologies for multiple postings)
*CALL FOR PAPERS* <https://elex.link/elex2023/call-for-papers/>
*eLex 2023: Electronic lexicography in the 21st century.* The topic of next
year's conference is Invisible Lexicography.
Dates: 27-29 June 2023 (with workshops on June 26th)
Venue: Hotel Passage, Brno, Czechia
Deadline for abstract submissions: January 31st 2023
Conference website: https://elex.link/elex2023/
Language of the conference: English
Format:
The conference will be organized as a hybrid event and while we encourage
everyone to participate on-site, we plan to provide live streaming and
recording of the event for registered participants.
Looking forward to seeing you all in Brno,
Miloš Jakubíček
in the name of the organising committee
Dear colleagues,
We are happy to announce the call for papers for our one-day special
session on “Multi-Perspectivist Data and Learning 2023” at the upcoming CD
MAKE 2023 conference:
https://cd-make.net/special-sessions/multi-perspectivist-data-and-learning/
***************************************************
Description, scope and aims
Many Artificial Intelligence applications are based on supervised machine
learning (ML), which ultimately grounds on manually annotated data. The
annotation process (i.e., ground-truthing) is often performed in terms of a
majority vote and this has been proved to be often problematic, as
highlighted by recent studies on the evaluation of ML models. Recently, a
different paradigm for ground-truthing has started to emerge, called data
perspectivism, which moves away from traditional majority aggregated
datasets, towards the adoption of methods that integrate different opinions
and perspectives within the knowledge representation, training, and
evaluation steps of ML processes, by adopting a non-aggregation policy.
This alternative paradigm obviously implies a radical change in how we
develop and evaluate ML systems: such ML systems have to take into account
multiple, uncertain, and potentially mutually conflicting views. This
obviously brings both opportunities and difficulties: novel models or
training techniques may need to be designed, and the validation phase may
become more complex. Nonetheless, initial works have shown that data
perspectivism can lead to better performances, and could also have
important implications in terms of human-in-the-loop and interpretable AI,
as well as in regard to the ethical issues or concerns related to the use
of AI systems.
The scope of this special session is to attract contributions related to
the management of subjective, crowd-sourced, multi-perspective, or
otherwise non-aggregated data in ground-truthing, machine learning, and
more generally artificial intelligence systems.
Invited contributions: full research papers and research in progress papers.
***************************************************
Topics of interest:
- Subjective, uncertain, or conflicting information in annotation and
crowdsourcing processes;
- Limits and problems with standard data annotation and aggregation
processes;
- Theoretical studies on the problem of learning from multi-rater and
non-aggregated data;
- Participation mechanisms/incentives/gamification for rater engagement and
crowdsourcing;
- Ethical and legal concerns related to annotation and aggregation
processes in ground-truthing;
- Creation and documentation of multi-rater and non-aggregated datasets and
benchmarks;
- Development of ML algorithms for multi-rater and non-aggregated data;
- Techniques for the evaluation of ML systems based on multi-rater and
non-aggregated data;
- Applications of data perspectivism and non-aggregated data to eXplainable
AI, human-in-the-loop AI and algorithmic fairness;
- Experimental and application studies of ML/AI systems on multi-rater and
non-aggregated data, in possibly different application domains (e.g. NLP,
medicine, legal studies, etc.)
***************************************************
Important dates:
Submission Deadline March 27, 2023 (AoE)
Author Notification June 01, 2023
Proceedings Version June 22, 2023 (AoE)
Conference August 29 – September 01, 2023
***************************************************
Special Session Chairs:
Federico Cabitza (University of Milano-Bicocca, Italy)
Andrea Campagner (University of Milano-Bicocca, Italy)
Valerio Basile (University of Turin, Italy)
Program Committee (provisional):
Nahuel Costa Cortez, University of Oviedo
Elisa Leonardelli, Fondazione Bruno Kessler (FBK)
Julian Lienen, Paderborn University
Gavin Abercrombie, Heriot-Watt University
Simona Frenda, University of Turin
Marília Barandas, Fraunhofer Portugal AICOS
Duarte Folgado, Fraunhofer Portugal AICOS
Barbara Plank, Ludwig Maximilian University of Munich
Tommaso Caselli, Rijksuniversiteit Groningen
***************************************************
Related readings
[1] Cabitza, F., Campagner, A., Basile, V. (2023)
Toward a Perspectivist Turn in Ground Truthing for Predictive Computing
Proceedings of the AAAI Conference on Artificial Intelligence
(extended preprint at: https://arxiv.org/pdf/2109.04270.pdf)
[2] V. Basile (2020)
It’s the End of the Gold Standard as we Know it. On the Impact of
Pre-aggregation on the Evaluation of Highly Subjective Tasks
Proceedings of the AIxIA 2020 Discussion Papers Workshop
[3] F. Cabitza, A. Campagner, L. M. Sconfienza (2020)
As if sand were stone. New concepts and metrics to probe the ground on
which to build trustable AI
BMC Medical Informatics and Decision Making
[4] Plank, B. (2022).
The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling
and Evaluation.
arXiv preprint arXiv:2211.02570.
Hello,
Could you please distribute the following job offer? Thanks.
Best,
Pascal
-------------------------------------------------------------------------------------
3-year PhD position in Computational Models of Semantic Memory and its Acquisition (Inria and University of Lille, France)
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 (angele.brunelliere(a)univ-lille.fr) and Pascal Denis
(pascal.denis(a)inria.fr).
The starting date of the position is 1 October 2022 or soon
thereafter, for a total of 3 full years.
Best regards,
Angèle Brunellière and Pascal Denis
[1] https://team.inria.fr/magnet/
[2] https://scalab.univ-lille.fr/
--
Pascal
----
Pour une évaluation indépendante, transparente et rigoureuse !
Je soutiens la Commission d'Évaluation de l'Inria.
----
+++++++++++++++++++++++++++++++++++++++++++++++
Pascal Denis
Equipe MAGNET, INRIA Lille Nord Europe
Bâtiment B, Avenue Heloïse
Parc scientifique de la Haute Borne
59650 Villeneuve d'Ascq
Tel: ++33 3 59 35 87 24
Url: http://researchers.lille.inria.fr/~pdenis/
+++++++++++++++++++++++++++++++++++++++++++++++
Dear Colleagues,
The Institute of Modern Languages at the University of Zielona Góra, Poland, announces a conference titled "Contemporary Trends in English-Language Studies". This year's edition will be held entirely online on May 18-19.
More information is available at: https://sites.google.com/view/ctiels/
Thank you!
Leszek