A fully funded PhD position is open at the University of Strasbourg (ICube). The position will be jointly funded by the French National Centre for Space Studies (CNES) and the Chair SDIA. The candidate will join the SDC research team under the supervision of Dr Thomas Lampert, the Chair of Data Science and Artificial Intelligence, and join his international team to develop novel deep learning approaches to domain invariant representation learning for satellite image time-series (SITS).

Domain invariant interpretable representation learning for satellite image time-series

The goal of the project is to develop models for learning domain invariant representations using deep learning for the analysis of satellite image time-series.

It is difficult and expensive to annotate the huge amount of data generated by satellites, but this is needed for the success of deep learning algorithms. To overcome this, transfer learning and domain adaptation techniques will be developed to exploit unlabelled data. These techniques allow an algorithm’s performance to be improved with minimal (or potentially no) additional annotation, lowering the cost of deployment.

The successful candidate will have (or will soon obtain) an MSc in Computer Science or related subject. Experience with deep learning is required and experience with time series and/or remote sensing is a bonus.

Send a letter of motivation and your CV to Thomas Lampert and Gisèle Burgart ([email protected] and [email protected] - !remove the numbers!) with the subject beginning with [CNES PhD].

The application deadline is 15/3/2022 and the starting date will be September 2022 (or soon after).

Detailed Description: https://drive. google.com/file/d/1W92enhzhKLJ0_IjD4pSSMYHw-y6SxQdj/view?usp=sharing

The CIAD laboratory in Dijon is recruiting a postdoc for 12 to 18 months depending on profile as part of the ANR DALHAI project.

The objective of this project is to develop compact all-optical arithmetic and logical units (ALUs) exploiting the spatial and spectral distributions of 2D confined plasmon modes in planar cavities carved from ultrafine Crystals of Au or Ag. We initiated the development of a new modeling approach by combining generative antagonist network (GAN) approaches and logical reasoning to perform optical simulations and obtain experimental data.

The goal of this hybrid reasoning environment is to propose device geometries and excitation protocols to solve the reverse design of complex reconfigurable ALU. Nanofabrication, simulations, optical benchmarking, exploitation and reconfiguration of ALU proposed by AI will make it possible to physically build these ALUs. The experimental manufacturing and optical tests and numerical simulations of plasmonic ALU will be carried out by the ICB (CNRS, Dijon).

The results obtained open up a new and very innovative artificial reasoning path that we wish to explore with this post-doctoral position. The mission is spread over several phases.

1/ The first phase revolves around the AI approach put in place. This phase consists of analyzing the limitations of simulation tools in the field of plasmonics and nanophotonics and becoming familiar with the AI approach implemented.

2/ The second phase aims to automate the generation of a corpus of synthetic data by implementing the hybridization of artificial intelligence algorithms. 3/ The third phase of this post-doctorate consists in setting up symbolic and connectionist artificial intelligence approaches to solve problems applied to the field of nano-plasmonics such as the prediction of optical signals, the optimization of the efficiency of logic gates

The position is open for a start hoped in February 2022 over a period of 12 months to 18 months depending on the profile of the candidate.

The place of activity is the CIAD laboratory - University of Burgundy, 64 rue de Sully, 21000 Dijon

The salary is about 2500 euros gross per month.

This position is open to anyone with a PhD in Computer Science with good experience in machine learning/deep learning and possibly Semantic Web technologies. Fluency in written/spoken English is also required. A good publication record and strong programming skills will be a plus. Applications will be accepted until the position closes.

Applicants must send a full CV including a full list of publications, a cover letter stating their research interests, achievements to date and vision for the future, as well as letters of support or the names of 2 people who have worked with them.

Contact: Christophe Nicolle ([email protected])

Title : Combining graph embedding and topic modelling for ontology/KG learning from large scale data

Key words : Topic modelling, Knowledge graph learning, Ontology learning, Graph embedding, Deep learning

Description. Ontology learning from the web data is a major challenging topic within the semantic web field and many approaches have been developed to tackle it. However, due to sparsity and heterogeneity of data, they lack to provide good quality results with a high semantical relevance for humans. The post-doc work aims to define a new approach for ontology learning/knowledge graph learning by incorporating embedded knowledge graphs in a clustering technique (topic modelling) dealing better with the sparsity and the heterogeneity of texts available in the Web and the semantical relevance of the results.

The research domain of this post-doc position is model learning, linked data and graph embedding for ontology/knowledge graph learning from texts. Model learning/Topic modelling is one of the area expertise of the DUKe team (Data User Knowledge) of LS2N, one of the France's leading public research labs in digital sciences. Linked data, graph building from texts and knowledge graph embedding are fields of expertise of the Japanese Ichise Laboratory from the National Institute of Informatics (NII), one of the leading research institute in Japan.

Duration : 12 months from (1 January 2022 -31 December 2022) including a mobility of 3 months in Japan .

Localization : Polytech Nantes, France , Ichise Laboratory, Tokyo Japan

Salary: 2900€ gross monthly + mobility expenses in Japan, during three months, about 350.000 yens / month.

Application: Candidates should have a PhD in computer science or applied mathematics, with strong experience in machine learning and related coding ecosystems in python. A background in semantic web and probability/statistics would be a plus.

Applicants should send a full CV including a complete list of publications and completed projects, a cover letter, and letters of recommendation or the names of two people who have worked with them. Contact: Mounira Harzallah ([email protected], Fabrice Guillet [email protected]), DUKe, LS2N, France

Hello, As part of the CNRS internal mobility, an FSEP position Engineer-e of studies in production, processing, data analysis and surveys has been published on:

https://mobiliteinterne.cnrs.f r/ords/afip/owa/consult.affich e_fonc?code_fonc=G54010&type_ function=FSEP&code_dr=&code_bap=&code_corps=IE&nbjours=30&page=2&colonne_triee=1&type_tri=ASC

to be filled at the Dynamic Language Laboratory in Lyon, MSH-LSE. To your applications!

Chenu Laboratoire Dynamique Du Langage (UMR CNRS & Univ. Lyon2), MSH-LSE

Context The BIRD team of the LORIA laboratory (Nancy) and the company Keep In Touch (Strasbourg) are recruiting a post-doctoral fellow for a period of 18 months in the field of Analytic HumanResource: AI (machine learning) for the benefit of e-recruitment.

The objective of the joint research project is the automatic identification of non-formalized profile elements (company/offer and candidate) in job offers and CVs to improve the quality of recruitment.

Current approaches to recruitment rely on the information explicitly presented in CVs and job postings to automatically identify promising candidate profiles for a given job posting (positions held, technical skills, years of experience, employer, etc.).

However, important but not explicitly present elements come into play and must be taken into consideration for a successful recruitment.

For example, skills acquired in one field and transferable to another, skills/abilities not explicitly mentioned but inferred from the mentioned activities, business practices ...

Objectives of the post-doctoral fellow The post-doctoral fellow will have several objectives:

- Design a model for automatic identification of latent skills. This model will be based on machine learning and will operate a bank of resumes and personality tests.

- Design a model for creating a (argued) portrait of a company reflecting the practices, feelings, etc. around this company. This work will be based on data from partner companies (the exact nature of which is still being defined with the partners).

- Create an offer(company)/CV matching model exploiting the implicit elements and based on the two previous models.

The person recruited will be in contact with a data engineer in the company.

The person recruited will be an employee of the company and made available to the LORIA laboratory in Nancy (main place of work). Some trips are to be expected on Strasbourg.

Recruitment: as soon as possible

Skills required.

The person recruited will hold a PhD in computer science (specialty Artificial Intelligence and machine learning preferably), with knowledge in automatic language processing.

A strong ability to interact: understanding of needs and argumentation of choices (oral and written) is required.

Development skills are needed.

How to apply Send an email to armelle.brun @ loria.fr and antoine @ kit-rh.com by attaching

- CV (including the list of publications)

- a cover letter

- thesis pre-reports

- the 2 most significant publications

Context : VITA project on robotics in public spaces Goal : The VITA project aims at studying the deployment of robots in public space. One of the main issue faced in this project is to develop algorithms for path planning and navigation in very large maps.

We propose, 2 open research assistant positions of 6 months are open on path planning in very large maps.

Background : Scientific skills : AI Planning, A*, RTA*, …

technical skills : ROS is a plus but not mandatory.

Applications : send a CV to abdel-illah.mouaddib@unicaen. fr

Expected starting date : January 2022

End time : June 2022

Regards, Abdel-Illah Mouaddib

Machine Learning for Industrial and Agricultural Data

This project is part of the interface between the industrial and agricultural fields, and that of AI.

The successful candidate will have to process industrial and agricultural data from different companies. It will have to clean and classify the data, and test different AI models, in order to automate the machines.

Keywords : AI, Machine Learning, Deep Learning, Predictive Maintenance, Automation

Background and Objectives: The digitization of industrial and agricultural tools, and the profusion of associated data, have a lasting impact on the uses made of them in these fields. Advances in the development of automated data analysis and decision-making techniques require interdisciplinary work in disciplines such as machine learning algorithms, the statistical and computational principles that underpin these algorithms, data warehousing methods and their analysis.

In a context that encourages the digitization and optimization of industrial and agricultural processes, many companies are willing to develop and offer intelligent products to their customers in order to anticipate machine problems and/or automate them. In order to optimize the operation of machines, their performance, and reduce operating costs, industrial and agricultural technical staff must have at their disposal technological and organizational innovations that will help them in the management of their tasks and in particular in decision-making.

The recruited candidate will have to process industrial and agricultural data from different companies. It will have to clean and classify the data, and test different AI models, in order to automate the machines. Eventually, AI models, optimized, must be inserted into the machines.

Candidate Profile Education: Engineering school or University (bac + 5 or bac + 8) in Data Science / computer science / mathematics /applied physics

Key competencies: · Data processing mathematics and statistics

· Machine Learning/ Deep Learning

· Implementation of complex algorithms

· Technology environment: Python, R, C++, Matlab

· Fluent English (spoken, read and written)

Beyond scientific and technical skills, you will need to: · Demonstrate autonomy

· Show curiosity and be able to model a problem up to a clear set of concepts and hypotheses to test

· Taste for the communication of results and the popularization of science

· To be a force of proposals and to be able to seek solutions in areas of tacit and explicit knowledge

· Have a taste for R&D with a view to industrialization and optimization

E-mail address to which the candidate must send his application: [email protected]

Application deadline: 31/01/2022

The ForM@Ter Data and Services Hub(www.poleterresolide.fr), dedicated to the Solid Earth, is a component of the Terra Data research infrastructure.

Its creation responds to the massive influx of solid Earth data from new space missions, new types of ground, marine or airborne sensors, laboratory experiments, numerical modeling, digitization of Earth archives, etc.

In this context, to meet the needs of the scientific community, ForM@Ter facilitates access to data in the Solid Earth domain; provides powerful services and tools to access, process and analyze this data as well as derivatives on solid Earth.

Form@ter is looking for a Semantic Web Engineer to: - Participate in the design and creation of terminological and ontological resources to improve the discovery, access and processing of Solid Earth data as part of the data and services cluster ForM@Ter the Terra Data Research Infrastructure (data-terra.org).

- Contribute to setting up a working method for the maintenance and updating of these resources for each discipline of the solid Earth.

- Ensure their standardization in adapted formats and models (RDF, SKOS, OWL) and contribute to the implementation of software bricks allowing their dissemination and access.

- Also contribute to completing the description of the data exposed in the metacatalogue of the ForM@Ter portal (metadata sheets).

This work will be carried out on the basis of the recommendations issued by the IR Data Terra, in collaboration with the vocabularies, catalog and implementation of the Data Terra data warehouse work team.

They will also build on ongoing work in the framework of the RESIF-EPOS IR(www.resif.fr)and the European IR EPOS (European Plate Observing System www.epos-eu.org/).

The candidate will participate in the coordination of the work carried out within the framework of ForM@Ter on vocabularies and thesauruses with these three RIs.

Skills - Mastery of the structuring of knowledge (concepts and languages of the semantic web, construction and alignment of ontologies) and in the construction of a data web (concepts of linked data) - Good knowledge of interoperability standards and web data (W3C recommendations for the representation of knowledge)

- Semantic web technologies and languages: SPARQL, SKOS, RDF, RDFS, OWL.

- A good knowledge of the disciplines of the Earth solid would be very appreciated

- Written and oral

English Soft skills: - Possess the sense of listening, analytical, synthesis and popularization skills for the accompaniment of people in the process of description / semantization of their profession

- Good writing skills for reports and documents Technical.

- Know how to work in a team, in a network.

- Possess good organizational skills, autonomy and strength of proposal.

Additional information Desired level of education: Bac + 5 Desired experience: 1 to 4 years Duration of the contract: 18 months Expected date of hiring: January 1, 2022 Work quota: Full-time Remuneration: Between € 2,172 and € 2,293 More information on the position: https://bit.ly/3osCCxN

Post-Doctoral position

REEGAR: Is that Real enough? EEG markers of real vs virtual objects for an enhanced AR-BCI setting

Research Team: IMT Atlantique Brest, Inuit (INFO) and BRAIn (MEE) teams

Funding: 11 months (starting February 2022)

Keywords: Augmented Reality, Electroencephalography, Brain Computer Interfaces, Perception of Virtual Objects

Contact: Etienne Peillard ([email protected]), Guillaume Moreau ([email protected]), Giulia Lioi ([email protected])

Job Description The INUIT and BRAIn team at IMT Atlantique are seeking a highly qualified young researcher with experience and motivation in Augmented Reality(AR) and/or EEG signal processing.

This position is open by IMT Atlantique Brest (France). At the interface between AR and Brain Computer Interfaces (BCI) research, the project REEGAR aims at studying the impact of visual appearance of virtual objects on visual perception but also interaction capabilities with the help of EEG signals.

AR/VR presents itself as a direct way to connect the user with digital content. However, the presentation of this content still has many flaws, especially on the visual rendering. It remains quite easy to distinguish a real object from a virtual one: the latter will appear brighter, transparent, potentially badly bound to its environment, lacking shading and light reflections. In general, the realism of virtual objects is still an important issue in AR/VR. Moreover, it is possible that the realism of a virtual object, even very close to reality, negatively impacts its perception. In the same way as the Uncanny valley (Mori’s theory that the more similar an android robot is to a human being, the more disturbing its imperfections appear to us), a virtual object whose appearance is close to that of a real object may be more disturbing than a purely virtual object. Indeed, since we cannot interact in the same way with a real object as with a virtual one, this blurred boundary could disturb the user.

The general objective of this project is to define this boundary between real and virtual in an AR environment. Using EEG, we will aim at recognizing the nature of the object considered by the user. In the longer term, this recognition could allow to overcome the evaluation paradigm of objects in AR and thus 1) to improve the rendering of virtual objects in order to bring them closer to real objects in a way that is acceptable to the user and 2) to improve the interaction of the user with the objects by detecting in advance the nature (real or virtual) of the object with which he wishes to interact.

As very limited knowledge is currently available in the field, and the project is mainly exploratory.

The selected candidate will collaborate with the other members of the teams and their collaborators to 1) design an experimental protocol and AR environment 2) perform EEG signal acquisition in an AR setting 3) analyze multichannel EEG data to identify brain activity markers or correlates of virtual vs real objects.

Research Environment IMT Atlantique is a technological university and offers very competitive salary packages, with postdoc wages corresponding to a junior assistant professor level. Successful candidates will also benefit from 49 days of annual paid holidays.

The BRAIn team is a reference for its work at the intersection between deep learning (Vincent Gripon, Mathieu Leonardon) and neuroimaging (Giulia Lioi, Nicolas Farrugia). The Inuit team is specialized in Virtual-Augmented Reality and 3D interaction and involved members (Etienne Peillard, Guillaume Moreau) specialize into understanding the perception of virtual and augmented environments.

Applicant Profile

The Ideal applicant should have:

❖ Theoretical, technical and practical knowledge in signal processing

❖ Strong programming skills using Python and Unity

❖ Ability to work in a team

❖ Autonomy

❖ Mastery of English

As this project is at the intersection of the fields of AR and EEG, candidates will need to have expertise in one of them and enough curiosity and autonomy to train in the other.

How to apply Applicants should send their complete application package by email to one of the contacts that includes:

❖ Motivation letter

❖ Complete CV with publication list

❖ PDF of one representative paper (or slideshow) of the candidate in connection with this project.

❖ Recommendation letters (preferably directly sent by the mentor)

Hello An MCF position in computer science at ENSEA, sections 61-27, will be published in synchronized session 2022. The position will be attached in research at the ETIS laboratory(https://www.etis-lab.fr/),to one of the MIDI or Neuro teams, and in teaching at ENSEA(https://www.ensea.fr/fr).

An MCF position in Computer Science at ENSEA, sections 61-27, will be published in the synchronized session 2022. The position will be attached in research to the ETIS laboratory (https://www.etis-lab.fr/), to one of the MIDI or Neuro teams, and in teaching to ENSEA (https://www.ensea.fr/fr).

Contacts Recherche/Research – Olivier Romain, directeur ETIS / head of ETIS ([email protected]), Dan Vodislav, responsable équipe MIDI/head of the MIDI team ([email protected]), Alexandre Pitti, responsable équipe Neuro/ head of the Neuro team ([email protected]).

Teaching – Aymeric Histace ([email protected]).

Teaching Profile The recruited lecturer will be involved in more specific courses in the fields of Computer Science, in particular in the specialties "Computer Science and Systems" and / or "Signal and Artificial Intelligence" and / or "Electronics for Life and Ecosystems" in the last year of the engineering course. He/she will also be able to intervene in computer science courses within all ENSEA training courses. In addition, he/she will participate in the reflections carried out in the pedagogical department concerned and will be able to invest in responsibilities such as that of the department or in various missions related to the development of the institution at the national and international level.

Teaching profile The recruited MCF will be involved more specifically in teaching in the fields of Computer Science, in particular in the "Computer Science and Systems" and/or "Signal and Artificial Intelligence" and/or "Electronics for Life and Ecosystems" specialties in the final year of the engineering curriculum. He/she will also be able to intervene in Computer Science courses within all ENSEA training programs. In addition, he/she will participate in the reflections carried out in the pedagogical department concerned and will be able to invest in responsibilities such as that of the department or in various missions in connection with the development of ENSEA at the national and international level.

Search Profile AI has reported remarkable progress on a wide range of problems (e.g., machine translation, text-to-speech, robotics, etc.), but the main advances are based on increasingly resource-intensive and energy-intensive deep learning models. Recent studies estimate that the cost of calculating deep learning models is increasing exponentially, with an estimated 300,000-fold increase between 2012 and 2018. In comparison, the brain is far superior to current machine learning techniques in terms of computing and energy efficiency. In this context, the ETIS laboratory is looking for excellent candidates capable of understanding the issues related to AI research that takes into account the cost of calculating the different models, encouraging a reduction in the resources spent and therefore in the carbon footprint. In particular, ETIS is interested in finding more efficient ways to allocate a given budget to improve performance, or to reduce compute expenses with minimal performance reduction.

The Senior Lecturer will join either the MIDI team or the NEURO team of the ETIS laboratory to develop this research theme. Interdisciplinary research projects between the two teams on the computational models used or on particular areas of research will be particularly encouraged. The successful candidate will promote, animate and represent their research activities. The candidate will contribute to the influence of the ETIS laboratory, in particular with the actors and partners of ENSEA, the CY Cergy Paris University Grand Établissement, local stakeholders, competitiveness clusters and local authorities. He/she will also strengthen national and international collaborations.

For the MIDI team, the focus is on approaches that introduce space or time constraints when learning AI models (e.g., graph neural networks). In particular, data-efficient machine learning methods should expand our ability to learn in complex areas without requiring large amounts of data. A good result in this area often involves achieving performance similar to that of a basic model with fewer learning examples or fewer gradient steps. There are many general approaches that currently demonstrate the plausibility of the approach by exploiting the structural knowledge of our data (e.g., symmetry), or by applying data priming and augmentation techniques, or by using semi-supervised learning techniques. The MIDI team is particularly interested in learning graph structures, which are widely used for modeling real-world systems, including social media platforms, collaboration networks, biological networks, and critical infrastructure systems.

Regarding the NEURO team, approaches based on bio-inspired models to quickly learn causal and structural relationships (e.g. Bayesian inference, predictive coding), or on time scales (synchrony, continuous/incremental learning, point learning, attentional models, working memory), or on reasoning of hierarchical representations (symbol grounding, hierarchical reinforcement learning, meta-learning) are expected. This point of view can also process information from a biological and dynamic systems point of view: sparse coding, asynchronous neural networks, pulse neural networks, reservoir computing, small world networks, attentional networks, neuromodulation, etc.

Research profile AI has reported remarkable progress on a broad range of problems (e.g., machine translation, text-to-speech, robotics, etc.), but key advances rely on increasingly large and computationally-intensive deep learning models. Recent studies estimate that the computational cost of deep learning models is increasing exponentially, with a 300,000x increase estimated from 2012 to 2018. In comparison, the brain is well above current machine learning techniques in terms of computational and energy efficiency. In this context, the ETIS laboratory is looking for excellent candidates capable of understanding the issues related to AI research that takes into account the computational cost of different AI models, encouraging a reduction in resources spent and thus in the carbon footprint. In particular, ETIS is interested in investigating more efficient ways to allocate a given budget to improve performance, or to reduce computational expenses with a minimal reduction in performance.

The MCF will join either the MIDI team or the NEURO team of the ETIS laboratory to develop this research topic. Cross-research projects between the two teams on the computational models used or on particular domains of research will be particularly encouraged. The successful candidate will promote, animate and represent his/her research activities. The candidate will contribute to the influence of the ETIS laboratory, in particular with the actors and partners of ENSEA, of the Grand Établissement CY Cergy Paris Université, the actors of the territory, the competitiveness clusters and the territorial authorities. He/she will also reinforce national and international collaborations.

For the MIDI team, the focus is on approaches that introduce space or time constraints when training AI models (e.g., graph neural networks). In particular, data-efficient machine learning methods should extend our ability to learn in complex domains without requiring large quantities of data. A strong result in this area often involves achieving similar performance to a baseline with fewer training examples or fewer gradient steps. There are many general-purpose approaches that currently demonstrate the plausibility of the approach by exploiting structural knowledge of our data (e.g., symmetry), or by applying bootstrapping and data augmentation techniques, or by using semi-supervised learning techniques. The MIDI team is particularly interested in learning graph structures, which are widely used for modeling real-world systems, including social media platforms, collaborative networks, biological networks, and critical infrastructure systems.

Concerning the NEURO team, approaches based on bio-inspired AI models to rapidly learn causal and structural relationships (e.g., Bayesian inference, predictive coding), dealing with different time scales (synchrony, continuous/incremental learning, one-shot learning, attentional models, working memory), or with hierarchical representation reasoning (symbol grounding, hierarchical reinforcement learning, meta-learning) are expected. This view can also treat information from a biological and dynamical systems perspective: sparse coding, asynchronous neural networks, spiking neural networks, reservoir computing, small-world networks, attentional networks, neuromodulation, etc.

Sincerely, Dan Vodislav

Hello, An MCF computer science position at the University of Artois, profile machine learning / data mining, will be put to the competition in the spring, for the next school year 2022.

The person recruited will teach at the computer science department of the IUT of Lens at the University of Artois, and his research at the Center for Computer Research of Lens (CRIL). *** Research context ***

The CRIL, UMR CNRS 8188, is a laboratory composed of 67 people all working in Artificial Intelligence (AI).

Since its creation, this laboratory deliberately does not contain a team, so that people can work freely without any form of compartmentalization. The laboratory is simply structured in three main axes - Constraints, Data, Knowledge - which characterize the main themes in AI, studied in the laboratory. Specifically:

- The Constraints axis concerns the modeling and solving of problems under constraints.

- The Data axis concerns learning, as well as data processing and querying.

- The Knowledge axis concerns the representation of knowledge and reasoning.

The connection between these axes is great: many research projects within the CRIL feed on several axes, in order to benefit from different expertise. Among the projects of the laboratory,we have the explainability/interpretability of learning models from constraint-based techniques, the development of autonomous solvers and using reinforcement learning, the resolution of strategy games by learning and constraints, data mining

by encoding/SAT resolution, the elicitation of arguments by active learning, etc. *** Research Profile *** Through this recruitment, CRIL wishes to strengthen its openness to the theme of data, and particularly machine learning and/or data mining. Candidates' research projects must fall within these themes. Post-doctoral experience is expected. Female applications are strongly encouraged. To be recruited, the quality of the candidate's scientific file will be paramount.

For any information, you can contact us by email: - [email protected] (president of the COS) - [email protected] (vice-president of the COS) Sincerely, Frederic Koriche & Fahima Cheikh

Apply to a research project on "New digital forms for medical and surgical teaching"

The AIR project from the University of Rennes 1, in collaboration with University of Rennes 2, Inria, INSA and industrial IT companies, is one of the few national funded research projects to study new digital forms for teaching. Specifically, the AIR project aims to develop innovative operational solutions to increase and enrich pedagogical interactions through digital means. Within this project, the MediCIS/LTSI team aims 1) to develop innovative virtual reality based simulators to help learning non-technical medical and surgical skills, 2) to study data driven approaches for quantitative and objective assessment of skills based on machine learning and multimodal sensors, and 3) to promote the usage of and evaluate the developed systems and tools in medical contexts within the simulation center of the university and the collaborating medical simulation and training centers.

For this project, we are looking for one postdoc or research engineer and one PhD student in the area of virtual reality and artificial intelligence in medical and surgical training. The project will last three years. The MediCIS/LTSI lab is located within the medical university and is composed of researchers both from engineering and medicine working together on societal high value projects, in a context of responsible research, aware of social, environmental and ethical impacts.

Please contact [email protected] and [email protected] for more information (including CV and letter of motivation)

Pierre JANNIN https://medicis.univ-rennes1. en/

http://www.ltsi.univ-rennes1. fr/

LTSI, Inserm UMR 1099 - University of Rennes 1 MediCIS Team Faculty of Medicine 2,

Avenue du Pr. Léon Bernard 35043 Rennes Cedex, France Ph: +33 2 23 23 45 88 Fx: +33 2 23 23 69 17

Hello In order to strengthen its team, the ALAIA Joint Laboratory, intended for Language Learning Assisted by Artificial Intelligence, offers two research engineer positions (12 months).

ALAIA focuses on the expression and oral comprehension of a target foreign language (L2). In collaboration with its two partners, academic (IRIT) and industrial (Archean Technologie) as well as experts in language didactics, the missions will consist in designing, developing and integrating innovative services based on the analysis of the productions of L2 learners, the detection and characterization of errors ranging from phonetic to linguistic level. They will be refined according to the profile of the people recruited.

The expected skills relate to automatic speech and language processing as well as machine learning methods.

Details of the offer: https://www.irit.fr/SAMOVA/site/wp-content/uploads/2021/12/ALAIA_Ingenieur_de_Recherche_2022_Franc%CC%A7ais. PDF

Link to the LabCom: https://anr.fr/Projet-ANR-18- LCV3-0001 and https://www.irit.fr/SAMOVA/site/projects/current/labcom-alaia/

Salary: according to the current grid, profile and experience

Application deadline: both positions are to be filled at the earliest,

Starting position: early February 2022 if possible

Applications should be sent to Isabelle Ferrané([email protected])and Lionel Fontan([email protected]). Do not hesitate to contact us for more information.

Kind regards Isabelle Ferrané

Hello, The Inria center in Lyon is looking for a Contract Scientific Engineer in Machine Learning / Digital Biology.

The engineer will join the Néliphant project, which aims to develop new numerical AI methods for the search for new drugs (pharmacology) active against brain diseases.

Mission entrusted --------------- The project of this Technological Development Action (TDA) is to realize a prototype of a software workshop (framework) of data analysis and machine learning methods useful to predict the effect of a candidate molecule on genes / proteins, therefore its potential therapeutic action on neurological pathologies.

The databases used for this work will be the Library of Integrative Network-based Cellular Signatures (LINCS) as well as non-sensitive biological data acquired through micro-array or tissue experimentation.

The results of this development will be taken up and continued within the Beagle / Néliphant team but also other teams and projects via the opensource distribution of the framework realized.

The use of this framework will be intended for teams proposing new analysis methodologies or teams using existing analyses on new datasets.

Main activities The proposed work plan for the future engineer is as follows:

* Familiarize themselves with the databases expected as initial data sets (LINCS and experimental data) and carry out a state of the art of existing methods and tools for the analysis of these data.

* Perform a comparison of these methods on use cases resulting from neurological pathologies in a privileged way.

* Propose and implement a development infrastructure (framework) to combine and test the techniques studied with new methods resulting from research such as data mining, structural analysis or sequential learning.

All of this work and development should propose a published opensource environment allowing the development of heterogeneous methods for the analysis of biological data.

The environment will need to be versatile enough to integrate and represent multiple and heterogeneous datasets as well as new analysis methodologies. The work will be carried out in collaboration with the research teams (Beagle / Néliphant) and the Experimentation and Development Department (SED) of the Inria centre in Lyon.

Skills ---------- * Knowledge of data analysis methods: supervised learning, notion of neural network, notions of data mining.

* Knowledge in bioinformatics: manipulation and analysis of biological data (connectivity maps), modeling of biological interactions, knowledge of cellular mechanisms.

* Knowledge of data analysis frameworks: Python, Pandas, SkLearn, etc.

* Knowledge of big data management methods and tools (database manipulation).

* Knowledge of the tools for the representation and manipulation of formalized knowledge would be a plus.

* Interest in applications to biology/medicine.

Details and applications (before **26/12/2021**) ------------------------------ ----------------- https://jobs.inria.fr/public/c lassic/en/offers/2021-04254

IMDEA Networks Institute (http://www.networks.imdea.org) is an international research center located in Madrid, Spain, whose mission is bringing fundamental, cutting-edge contributions to the science of computer networks. As a growing, English-speaking organization that is establishing itself globally at the forefront of the development of future network technologies, IMDEA Networks offers a unique environment for pioneering scientists to develop their ideas across many research areas, under the lead of a multinational team of highly reputed faculty members.

IMDEA Networks is inviting applications for fully-funded PhD positions, open to outstanding undergraduate and postgraduate candidates interested in carrying out vanguard research in networking.

Applicants are required to have: - a BSc, MSc or equivalent degree in Computer Science, Electrical Engineering, Computer Engineering, Telecommunications, Telematics or a related field (awarded by the joining date)

- excellent academic transcripts

- fluency in spoken and written English

- a strong motivation to carry out a career in academic or industrial research.

Successful candidates will enjoy: - supervision throughout the PhD program by a researcher of international standing

- access to state-of-the-art resources and facilities for networking research

- interactions with leading academic and industrial partners in the context of research projects

- the freedom to focus on research activities, with full administrative support and no teaching duties

- a collaborative, multi-cultural and English-speaking environment

- the prospect to present the results of their research at top-tier venues in networking

- support to take innovations to market

- assistance with all aspects of immigration and relocation, including support with VISA applications

- a competitive salary at the European level, between 22,000 and 26,000 EUR/year before taxes, with an excellent value with respect to the cost of life in Spain

- benefits that include access to comprehensive public healthcare and unemployment compensation, fully paid social security and pension contributions

- very good opportunities for employment upon graduation in leading academic and industrial organizations worldwide, as proven by the current positions of IMDEA Networks alumni (https://www.networks.imdea.org/people/alumni-network)

- the unrivaled quality of life of the Madrid region.

Specific PhD positions are open on the following subjects: - Edge/cloud design, modeling and integration in intelligent cellular networks (advised by Vincenzo Mancuso)

- Experimental AI for networks (2 positions, advised by Marco Fiore)

- Modelling, Decision Making, and Learning in Edge Networks (advised by Jaya Champati)

- Mobile Network Communications (advised by Albert Banchs).

Full details on these positions are available at our institutional job opportunities webpage (https://networks.imdea.org/job-opportunities/). Inquiries on each position can be directed to the associated advisor. IMDEA Networks is an equal employer, and encourages qualified female candidates to apply.

Applications composed of a CV, a brief research statement, and the contact details of two referees shall be submitted by January 15, 2022 (14:00 CET) through the IMDEA Networks Institute hiring portal (https://careers.networks.imdea.org/). Applicants are invited to explicitly indicate their preferred advisor(s) as part of their application.

Marco Fiore Research Associate Professor IMDEA Networks Institute Avda del Mar Mediterraneo 22 28918 Leganés, Madrid, Spain phone: +34 91 481 6926 mail: [email protected] web: https://networks.imdea.org/p/marco-fiore/