The french CEA (Commissariat à l’Energie Atomique et aux Energies Alternatives) is looking for a Postdoctoral Fellow to join its laboratory of semantic analysis of texts and images.
The person hired will integrate an interdisciplinary team aiming that rely on predictive and generative artificial intelligence for biology by exploiting deep contextual language models of biological sequences, which representations generalize to several applications like the prediction of mutational effects.
Exponential growth in sequencing throughput together with the sampling of natural (uncultured) populations are providing a deeper view of the diversity of proteins sequences across the tree of life. Proteins are molecular engines sustaining cellular life and the unobserved determinants of their structure and function are encoded in the distribution of observed natural sequences. Therefore, such vast amounts of (unlabelled) sequences provide evolutionary data that can form the ground for unsupervised learning of predictive and generative models of biological function.
Our focus here will be to train high-capacity Transformer-based language models on sequence data, in a way analogous to what is done in natural language understanding, where the semantics of words is determined from the contexts in which they appear in sentences. Intrinsic organizing principles captured in the resulting representations can then be applied in transfer learning settings to different prediction sub-tasks using limited experimental data, like the effect of sequence variation on function. Following promising recent results, we plan to also explore zero-shot inference with no additional training and/or supervision from experimental data.
This project will be an excellent opportunity for a candidate who is looking to contribute to cutting-edge research and to train with experts in the field. We are seeking a detail-oriented computer scientist and problem solver passionate in science.
* Tune and optimize existing unsupervised transformer-based language models for protein sequences.
* Develop and optimize code and machine learning algorithms for predictive models.
* Integrate and analyze large data volumes.
* Interact continuously with scientists in an interdisciplinary team.
* Ph.D. or M.Sc. in a quantitative discipline, e.g. Applied Mathematics, Computer Science, Computational Biology, Physics or a closely related discipline.
* Experience with Python, open-source software libraries for machine learning and Linux (file systems, shell, hardware/software monitoring, etc). * Strong mathematical background and analytical skills.
* Effective organizational skills, e.g. the ability to prioritize work and contribute to the planning of a program of scientific research.
* Demonstrated interpersonal skills including both the ability to work independently and perform collaborative research in an interdisciplinary team environment.
* Good oral and written communication skills.
Preferred: Previous experience with transformer-based techniques for NLP pre-training and unsupervised transformer language models
This 2 years position is open to a range of candidates from recent college graduates to more experienced scientists (e.g. post-docs) – the chosen candidate's salary will be commensurate with their level of education, skills, and experience. Other benefits include:
- 48 days of paid holidays
- on-site subsidized restaurant
- partial remote work is possible, up to 3 days per week and 100 days per year
- CEA contribution to the personal company savings plan
We are based on the Paris-Saclay research campus in the south of Paris.
Interested candidates should submit a resume and short cover letter to [email protected]
ABOUT US : About CEA LIST: https://list.cea.fr/en/
About the LASTI lab: https://kalisteo.cea.fr/index.php/ai/ https://kalisteo.cea.fr/index.php/textual-and-visual-semantic/
About Genoscope: https://www.genoscope.cns.fr
A fully-funded, three-year PhD position is available at the Computer Science Lab (LERIA) of the University of Angers (France).
PhD subject: “Parallel Constraints Solving"
A complete description of the subject is available on https://filesender.renater.fr/?s=download&token=45d396b0-7594-4cde-8957-b8fdb3f9136a
Profile: Master or similar degree in computer science is mandatory. As the thesis proposal lies at the intersection of Constraint Programming and Parallel Programming, the candidate should have a strong background in some of these topics.
Beginning of the Thesis: september 2023
Co-directors of the Thesis: Frédéric Lardeux, Eric Monfroy, and Jean-Michel Richer
To apply: Complete application with CV, recommendation(s) letter(s), academic results to be sent to: [email protected]
We invite applications for a fully funded PhD position for 3 years at the IRIT laboratory and the University of Toulouse, Paul Sabatier, France, in the context of the recently funded project AT2TA on Analogy Making.
Analogy Making is a remarkable cognitive capability during which similarities and differences between two parallel situations are exploited in order to draw a common "essence" allowing us thus to categorize an object or a particular situation to a preexisting concept or create a new one.
Reasoning by analogy allows us thus to transfer our understanding of a previous situation to a new one and appropriately adapt it. It has been argued that analogy making lies at the core of cognition (Hofstadter 2001) and has recently drawn the attention of Deep Learning pioneers (Chollet 2017, LeCun 2022).
In Natural Language Processing analogies usually take the form of a quadruplet a:b :: c:d traditionally expressed as "a is to b as c is to d" (for example, Paris is to France as Berlin is to Germany). Most of the extant work considers a, b, c, d as word embeddings and then relies on geometrical properties of those embedding in a higher dimensional space in order to recognise a quadruplet as an analogy or to generate a d such that a:b :: c:d forms analogy given a, b and c. Despite the importance of analogies, most works in NLP do not consider analogies between sentences and do not concentrate on the underlying latent relations that form the common essence between pairs (a,b) and (c,d). The successful PhD candidate will work on computational models which can identify analogies between sentences or even bigger chunks of text with a particular focus on the identification of common latent relations which are also an essential part for an explainable AI.
The successful candidate should hold a Master's degree in computational linguistics or computer science or cognitive science and has prior experience in word embedding models or deep learning approaches in general. The candidate should have strong programming skills and expertise in machine learning. The position is affiliated with the IRIT laboratory at Toulouse and there will be frequent interactions with researchers at the Loria laboratory in Nancy in the context of the AT2TA project.
Applications will be considered until the position is filled, but applicants are encouraged to apply as early as possible since applications will be considered at the moment of reception.
Applications, in English or French, should include a detailed CV, a letter of motivation and at least two recommendation letters. Applications should be sent to Stergos Afantenos (stergos.afantenos at irit.fr). The starting date of the position will be in January 2023, but can be filled earlier if need be.
More information can be found here: https://cloud.irit.fr/index.php/s/OpwyvCBzadRFKxY
Postdoc Offer(12 months)
Start: As soon as possible
Supervision : Mounîm A. El Yacoubi and Dijana Petrovska
Team : Institut Polytechnique de Paris / ARMEDIA / SAMOVAR
Subject : Deep Learning-based Voice Digital Markers for Early Detection and Stratification of Parkinson Disease
Profile PhD thesis : Bac +5 in Data science or equivalent
Skills :
Data Science, machine learning, deep learning
Programming in Python and in particular the libraries of machine learning and deep learning (PyTorch, Tensorflow, Scikit-learn, etc.)
Good Level in English (Optional) Background in the medical field
Location: Paris-Dauphine University, France
Encadrantes / Contacts : Elsa Negre, MCF HdR, LAMSADE ([email protected]. fr) et Olivia Tambou, MCF HdR, Cr2D ([email protected])
Keywords: AI, Decision-making systems, Law.
Problem: Can we imagine a decision-making system as a support for access to the law? Illustration around the European regulation on AI
The purpose of this doctoral research project would be to imagine a decision-making system (decision support) based on the analysis of the how a legal text has been adopted and then interpreted. The point of The starting point would therefore be to access, process and analyze/interpret a large Mass of legal data containing all the preparatory work for the development of a legal text.
A decision-making system, like the law, is based on different Steps that can help stakeholders find, among other things, relevant information to improve their decision-making. It is therefore a question of automated decision-making support, which remains the responsibility of the stakeholder.
Such a project starts from the paradigm that a decision-making system, all Like legal analysis, involves extracting from a mass of data, information, and to create knowledge. It will therefore be a question of seeing in to what extent a decision-making system could support right.
Applications: Interested candidates are invited to send:
- a project of 2 pages maximum according to their understanding of the problem,
- a CV,
- their transcripts (Master 1 and 2) with rankings (Master 2 also, possibly partial), and
- several letters of recommendation;
as soon as possible, before January 15, 2023.
I would be grateful if you would publish this job offer on fixed-term contracts, likely to interest young graduates, engineers or post-docs in the fields of Data Science and Machine Learning.
This job contributes to a research project funded by the Occitanie Region, on the physiological determinants of arduousness at work. The main place of work is IRIT in Toulouse.
The detailed offer can be downloaded at the following address: https://cloud.irit.fr/index. php/s/UKWtOnfX1qM4vPZ/download
The ALMAnaCH lab at Inria Paris is looking for candidates for full-time permanent researchers in natural language processing (NLP) and digital humanities (DH) to join us in the beautiful city of Paris. We welcome profiles for both junior and senior positions.
A PhD and at least one post-doctoral experience in NLP or ML for NLP are required for junior positions, whereas a longer track record is required for senior positions.
Inria is the French national research institute for computer science and applied mathematics. World-class research, and, where relevant, transfer to industry and start-up creation are its main missions.
The ALMAnaCH lab focuses on NLP and DH, at the crossroads between theoretical computer science, machine and deep learning, and linguistics. More about ALMAnaCH: http://almanach.inria.fr/index-en.html — also, you can follow @InriaParisNLP on Twitter and Mastodon.
Working at ALMAnaCH means working in a diverse and inclusive team within an exceptional scientific environment, next to other leading Inria research teams in Computer Vision, Machine Learning and more. Upon arrival, junior permanent researchers will receive a welcome package including a full PhD grant and expenses.
You can find more information here: http://almanach.inria.fr/recruitment-en.html.
If you are interested, please contact me by the end of the year at benoit.sagot<αt>inria.fr.
Please allow me to share the announcement of the European Cofund programme SOUND. AI (https://soundai.sorbonne-universite.fr/dl/home), see below, to circulate to your students looking for a PhD in France.
Project: SmartFCA (ANR), 2022-2026
Laboratory: IRISA, Rennes
Team: LACODAM
Duration: 24 months, beginning between February and April 2023
Contact: [email protected], [email protected]
Keywords: data mining, Formal Concept Analysis (FCA), graphs, interoperability, linguistic data
Background: Formal Concept Analysis (FCA) [1] is a method of knowledge discovery
It is used in data analysis, data mining, classification or information retrieval tasks; and applied in various fields such as life sciences , humanities or linguistics. Multiple FCA extensions have been proposed by different teams to process complex data such as sequences, trajectories , trees or graphs [2].
Beyond the theoretical and practical locks, there is a problem of interoperability between these different extensions, which hinders their adoption and composition in workflows .
An important objective of the SmartFCA project is to make these FCA extensions interoperable by encapsulating them in software components with conceptually and technologically compatible interfaces. It is also about implementing a platform allowing the construction of workflows from components.
The partner IRISA/Rennes is responsible for the Graph-FCA component [3], an extension of FCA to relational data and graphs. We work closely with the partner ICube/Strasbourg who is responsible for the component for another FCA extension to relational data, RCA (Relational Concept Analysis) [4].
Another objective of the project is to develop use cases in various fields, for their intrinsic interest and to evaluate the platform developed. IRISA/Rennes will develop use cases on linguistic data from languages with little resources (Breton [5,6] and Georgian [7] in particular).
Subject: After a familiarization phase with Graph-FCA and its current implementation , as well as RCA, it will be a question of collaborating with ICube/Strasbourg to design a compatible interface between the two FCA extensions (I/O modeling, option setsIt will then encapsulate the existing implementation of Graph-FCA in a RESTful API, in accordance with the standards established as part of the project.
The applicant is expected to collaborate with the other project partners in the establishment of these standards, and to be proactive. It will also be necessary to develop test and demo interfaces of the Graph-FCA component so as not to depend on the platform which will only be completed towards the end of the project.
The candidate must also provide technical support and be proactive in the use cases in linguistics (no knowledge of linguistics is required). This includes assistance in the preparation of data, the application of the Graph-FCA component and other components developed in the project and the enhancement of results, i.e. knowledge extracted from the data.
Candidate profile:
We are looking for a candidate motivated by research & development experience as part of an academic research project. The required training is a PhD or Master's degree in Computer Science.
Expertise required for the position: - web programming, especially backend and Node.js: design, development, configuration and documentation
- data models, including relational and graphs
- development tools and methods
- collaborative work - writing of technical reports and oral
Desired knowledge or experience: - knowledge extraction (data mining, data mining, classification)
- Caml programming or other functional language (Haskell, Scala, ...)
Expected qualities: autonomy, rigor, ability to collaborate face-to-face and remotely with several teams, strength of proposal
Hello, IRISA - CNRS (Rennes, France) offers a post-doc position (18 months) in NLP.
The recruited person will work on the hybridization between symbolic and deep learning for text, and on the robustness of classifiers as part of an ASTRID/AID project on disinformation.
Details and application: https://emploi.cnrs.fr/Offres/ CDD/UMR6074-VINCLA-008/Default.aspx
The admissibility of the application is potentially subject to the examination of the Defence Innovation Agency.
Contact: [email protected]
IRISA-CNRS (Rennes, France) offers a post-doc position (18 month contract) on NLP. The recruited researcher will work on symbolic/deep learning hybridization for text and on classifier robustness in the framework of an ASTRID/AID funded project about disinformation.
Details and application: https://emploi.cnrs.fr/Offres/ CDD/UMR6074-VINCLA-008/Default.aspx?lang=EN
The application may be reviewed by the Agence Innovation Defense.
Contact: [email protected]
Hello, The Lattice laboratory is recruiting a computer development engineer through the so-called FSEP procedure, that is to say that the position is open to mobility, mainly internally at the CNRS.
This is NOT an external recruitment, you must already be a civil servant to be able to apply.
The announcement (with application details) is available here:
https://mobiliteinterne.cnrs.fr/ords/afip/owa/consult.affiche_fonc?code_fonc=H54009&type_function=&code_dr=2&code_corps=IE&code_bap=E&nbjours=&page=1&colonne_triee=1&type_tri=ASC
Key skills are mastery of python and, if possible, current NLP (natural language processing) techniques.
The Lattice laboratory is located in Montrouge (metro Mairie de Montrouge); He is generally considered to be friendly and dynamic.