Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Laboratoire Parole et Langage (UMR7309)
Durée : 24 mois
Contact : laurent.prevot@univ-amu.fr
Date limite de publication : 2022-02-14

Contexte :
The long-term goal of SUMM-RE is to improve algorithms for automatic meeting summarization and meeting minutes. The central hypothesis of the project is that such systems will benefit greatly from exploiting rich information carried by discourse relations (Explanations, Questions/Answers, Corrections…) and discourse structure (in the form of graphs). One of the major objectives of the project is therefore to develop an incremental discourse parser for spontaneous conversation, building on extant work by SUMM-RE members using weak supervision (Badene et al. 2019). Discourse parsing will be done on English (the AMI corpus) and French data, but the principal focus will be on a 100h corpus of meetings in French whose creation will be completed by the time the postdoc starts.

SUMM-RE: anr.fr/Projet-ANR-20-CE23-0017, https://labs.linagora.com/summ-re/

Sujet :
The postdoc recruited for this position will be in charge of (i) adapting models of discourse segmentation (e.g. Muller et al. 2019) to meeting-style conversation by building on recent advances with weak supervision (Gravellier et al. 2021) and integrating both speech and acoustic parameters in the segmentation model; (ii) applying insights from discourse segmentation, which provides the foundation for discourse parsing, to improve the incremental discourse parser; (iii) considering and developing mitigation strategies for working directly on ASR output (rather than on gold human transcribed data) for both discourse segmentation and parsing.

Profil du candidat :
Given these tasks we are looking for a candidates with as many of the following skills as possible:

– Experience with speech and ASR, and conversational speech in particular

– Dialogue/conversation/interaction analysis and modeling

– Machine Learning, in particular Weakly Supervised and Unsupervised approaches

– Multimodal (speech + text) Deep Representations for Natural Language Processing

– Multilingual model transfer

Formation et compétences requises :
Phd in Computational Linguistics / Natual Language Processing / Machine Learning.

A minimal command of French is desirable as the postdoc will be required to handle a large French corpus; mastery of French is, however, not required.

Adresse d’emploi :
The postdoc will ideally be hosted by the Laboratoire Parole et Langage (LPL), though exceptions will be considered for candidates who wish to be based at IRIT.

A curriculum vitae and a list of publications should be sent to Laurent Prévot (laurent.prevot@univ-amu.fr) no later than February 18th, but we strongly encourage potential candidates to submit their applications as soon as possible, as we might fill the position earlier.

Laboratoire Parole et Langage: https://www.lpl-aix.fr/en/welcome-to-lpl/

LINAGORA Labs: labs.linagora.com

MELODI @ IRIT : https://www.irit.fr/departement/intelligence-artificielle/equipe-melodi/

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BII, A*STAR, Singapore
Durée : + 12 mois
Contact : malay_singh@bii.a-star.edu.sg
Date limite de publication : 2022-02-28

Contexte :
We are looking to hire a postdoc to work on an interdisciplinary project involving the development of AI models to quantify genetic intra-tumor heterogeneity in tissue whole slide images. This project is between the Bioinformatics Institute (BII, A*STAR), Genome Institute of Singapore (GIS, A*STAR), National Cancer Centre of Singapore (NCCS) and Singapore General Hospital (SGH). This project is supported by datasets derived from more than 5000 patients and strong clinical domain knowledge. Prior experience in deep learning is not required although candidates with deep learning experience are welcome. Training on deep learning is provided on the job. Please send your interest and CV to Malay Singh (malay_singh@bii.a-star.edu.sg) and Mahsa Paknezhad (mahsap@bii.a-star.edu.sg).

Sujet :
Position:
Postdoctoral position for developing an AI model to learn and quantify genetic intra-tumor heterogeneity in the tissue whole slide images.

Project Description: We are looking for a postdoctoral researcher to work with us on a collaboration project between the Bioinformatics Institute (BII, A*STAR), Genome Institute of Singapore (GIS, A*STAR), National Cancer Centre of Singapore (NCCS), and Singapore General Hospital (SGH). This project is supported by datasets derived from more than 5000 patients and strong clinical domain knowledge. We aim to design an AI model to quantify intra-tumor heterogeneity in histopathological tissue whole slide images. Cancer cells evolve into genetically diverse clonal populations in response to immune suppression via random mutation. High genetic diversity within the tumor cells leads to poor patient prognosis and is known as genetic intra-tumor heterogeneity (ITH). ITH is usually quantified via genomic methods. In this project, we will develop an AI model to quantify ITH using the imaging modality. We are looking for a creative mind with strong communication skills and a team player who can lead the project from forming ideas to methods development leading to publishing results in top-tier journals.

Profil du candidat :
What is in it for you?
You will be working in a team of AI researchers who have a deep understanding of the fundamentals of deep learning and have considerable experience in applying deep learning to different problems. Our group has a well established reputation in developing AI solutions for digital pathology. You will have the opportunity to learn and hone your AI skills through this project as well as by learning from other on-going projects in the team. You will be trained to be in the very niche area of applying deep learning for digital pathology applications. You will learn both AI and digital pathology knowledge in this project. This will be the differentiating factor for you as an AI scientist at our lab. You will also learn to sharpen your communication, collaboration, project management and leadership skills.

Responsibilities
1. Develop AI methodologies to perform computer assisted diagnostics for digital pathology.
2. Work closely with clinicians to fully understand the digital pathology datasets including histology slides as well as genomic data.
3. Design and develop the AI model to quantify the ITH in the histopathological images.
4. Design and conduct the experiments to assess the proposed AI models’ performance.
5. Collaborate with the team members via discussions, study groups, guiding students/ interns/research officers.
Periodically present the progress to the group and submit the research findings to top-tier journals and conferences

Formation et compétences requises :
1. Ph.D with a strong background in Computer Science/Mathematics/Statistics/Biomedical Engineering or relevant fields.
2. Prior knowledge in machine learning and prior domain knowledge in digital pathology is NOT REQUIRED. Training for these domains will be provided on the job.
3. Experience and versatility in programming especially in python.
4. Familiarity with PyTorch and/or TensorFlow is NOT REQUIRED but would be a plus.
5. Good communication skills, a team player and willing to share ideas and knowledge with peers.
6. Candidates should be able to work in a fast paced environment.

For more information, please visit the CVPD websites: http://web.bii.a-star.edu.sg/~leehk/ and
https://www.a-star.edu.sg/bii/research/ciid/cvpd

Adresse d’emploi :
BII, A*STAR, Singapore

Document attaché : 202201040827_JD_MS.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BII, A*Star, Singapore
Durée : +12 mois
Contact : mahsap@bii.a-star.edu.sg
Date limite de publication : 2022-02-28

Contexte :
We are looking for a postdoctoral researcher to work with us on an exciting collaboration between the Bioinformatics Institute (BII), the Institute for Infocomm Research (I2R) and Singapore General Hospital. The objective is to design an AI model that can help hospitals by learning from our dataset which contains a large set of cancer types to perform a diverse set of diagnostic tasks such as cancer grading and gland segmentation. Prior experience in deep learning is not required although candidates with deep learning experience are welcome. Join us if you are excited about improving the quality of healthcare and interested in learning AI skills. Please send your interest and CV to Mahsa Paknezhad (mahsap@bii.a-star.edu.sg) and Eddy Tan (eddy_tan@bii.a-star.edu.sg).

Sujet :
Position:
Postdoctoral Research Fellow position for developing an AI model to learn heterogeneous tasks in parallel. An application to digital pathology.

Project Description:
Training deep learning (DL) models on multiple heterogeneous tasks is one of the main steps in the direction of offering robust and generalizable AI solutions. We are seeking a postdoctoral research fellow to participate in an exciting collaboration between the Bioinformatics Institute (BII), the Institute for Infocomm Research (I2R) and Singapore General hospital. The aim is to design an AI model that can help hospitals by learning a diverse set of diagnostic tasks such as breast cancer grading and prostate gland segmentation as tasks are introduced through time. An AI model that is sufficiently smart to optimise the required network resources and to reduce the necessary dataset size to deliver a desirable performance for each task. To achieve these objectives the proposed AI model should be novel in many aspects. For instance, it should automatically decide which part of the network to assign to a new task, it should automatically expand itself if necessary to learn a new task, it should find similarities between tasks and share resources between similar tasks and it should avoid sharing resources between dissimilar tasks. We look for a creative mind with strong communication skills. A team player who can lead the project from forming ideas, to development, analysis and publishing results in top-tier journals.

Profil du candidat :
What is in it for you?
You will be working in a team of AI researchers who have a deep understanding of the fundamentals of deep learning and have considerable experience in applying deep learning to different problems. You will have the opportunity to learn and hone your AI skills through this project as well as by learning from other on-going projects in the team. You will acquire a deep knowledge of the cutting edge techniques in continual learning, reinforcement learning, and image processing. You will also learn to sharpen your communication, collaboration, project management and leadership skills.

Responsibilities:
(1) Maintain multiple histopathology datasets belonging to different healthcare problems
(2) Work closely with clinicians to fully understand the healthcare problems and the histopathology datasets
(3) Develop a novel AI algorithm that can expand (if necessary) and train parts of a neural network on different healthcare problems in parallel while providing efficiency in terms of task performance and network resources
(4) Carefully design experiments for assessment of the proposed AI algorithm
(5) Collaborate with peers, supervise interns and research officers
Periodically present the progress to the group and submit the research findings to top-tier journals and conferences

Formation et compétences requises :
Requirements:
(1) PhD in areas such as Computer Science, Machine Learning, Deep Learning, Computer Vision, Mathematics, Probabilities
(2) Sufficient experience in programming in python
(3) Familiarity with PyTorch or Tensorflow libraries is NOT REQUIRED but would be a plus
(4) Prior knowledge in deep learning is NOT REQUIRED but would be a plus
(5) Familiarity with Reinforcement Learning is NOT REQUIRED but would be a plus
(6) Good verbal and written communication and troubleshooting skills
(7) Curious, detail oriented, and analytical, with a proven ability to learn quickly
(8) A team player who is willing to share ideas and knowledge with peers
For more information, please visit the CVPD websites: http://web.bii.a-star.edu.sg/~leehk/ and https:// www.a-star.edu.sg/bii/research/ciid/cvpd

Adresse d’emploi :
BII, A*Star, Singapore

Document attaché : 202201040823_JD_MP.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BII, A*STAR, Singapore
Durée : +12 mois
Contact : eddy_tan@bii.a-star.edu.sg
Date limite de publication : 2022-02-28

Contexte :
We are looking to hire a postdoc to work on an exciting interdisciplinary collaboration project between the Bioinformatics Institute (BII, A*STAR) and the Institute of Bioengineering and Bioimaging (IBB, A*STAR). In this two years project, we are aiming to develop a deep learning model for the prediction of potential antimicrobial agents using a combination of well-established public datasets and an in-house database. Prior knowledge in deep learning is NOT REQUIRED, although candidates with deep learning expertise are welcomed. Training on deep learning is provided on the job.

Sujet :
Artificial intelligence (AI) techniques have been employed in the niche field of macromolecular therapeutics to accelerate the development of new, highly selective antimicrobial polymers. However, current AI methods utilize simplistic representations in creating models to identify non-hemolytic antimicrobial polymers. As such, we at the Bioinformatics Institute (BII, A*STAR) are looking to hire a postdoc to work on an exciting interdisciplinary collaboration project with the Institute of Bioengineering and Bioimaging (IBB, A*STAR). In this two years project, we are aiming to develop a deep learning model for the prediction of potential antimicrobial agents using a combination of well-established public datasets and an in-house database. You will be working among a team consisting of people with different domain expertise such as biologists, polymer chemists, bioinformaticians, and AI scientists. Therefore, the ideal candidate should be a team player who can formulate and execute creative ideas for a given problem. Join us if you are excited about improving the quality of healthcare and interested in learning AI skills.

Responsibilities:
1. Assist in the development and maintenance of an antimicrobial database
2. Develop and implementation of novel AI algorithms for the prediction of antimicrobial and hemolytic activity
3. Periodically present the progress to the group and submit the research findings to top-tier journals and conferences

For more information, please visit the CVPD websites: http://web.bii.a-star.edu.sg/~leehk/ and
https://www.a-star.edu.sg/bii/research/ciid/cvpd

Interested applicants are welcome to email a full CV and a one-page letter of intent summarizing past experience and strengths as well as contact details of two referees to Eddy (eddy_tan@bii.a-star.edu.sg), or Malay Singh (malay_singh@bii.a-star.edu.sg).

Profil du candidat :
You will be working in a team of AI researchers who have a deep understanding of the fundamentals of deep learning and have considerable experience in applying deep learning to different problems. You will have the opportunity to learn and hone your AI skills through this project as well as by learning from other on-going projects in the team. You will be trained to be in the very niche area of applying graphical neural networks for macromolecular therapeutic applications. This is the differentiating factor for you as a postdoctoral research fellow at our lab. You will also learn to sharpen your communication, collaboration, project management, and leadership skills.

Formation et compétences requises :
Basic Requirements:
1. Ph.D. in Computer Engineering, Computer Science, Mathematics, Statistics, or related discipline.
2. Proficient in python programming
3. Excellent communication (verbal and written) and presentation skills
4. Curious, detail-oriented, and analytical, with a proven ability to learn quickly
5. Ability to work as a team player, which includes the willingness to contribute ideas and knowledge with peers
6. Ability to adjust according to the pace of the project and its changing requirements.

Following skill sets are optional but will be advantageous for applicants to highlight any relevant experience(s) in the submitted CV.
1. Experience in machine/deep learning with a focus on graphical neural networks
2. Knowledge in chemistry, biochemistry, and antimicrobial agents
Having worked in multidisciplinary teams

Adresse d’emploi :
BII, A*Star, Singapore

Document attaché : 202201040817_JD_ET.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : AgroParisTech – Département MMIP (Modélisations Ma
Durée : Poste statutaire
Contact : antoine.cornuejols@agroparistech.fr
Date limite de publication : 2022-04-30

Contexte :
Un poste de Professeur dans le domaine de l’apprentissage automatique / intelligence artificielle pour les sciences du vivant, de l’environnement et de l’agronomie sera ouvert à AgroParisTech, membre fondateur de l’Université Paris-Saclay, au titre de la campagne de recrutement du printemps 2022.
L’intégration se fera au sein de l’UMR MIA-Paris (AgroParisTech / INRAé) dans l’équipe EKINOCS pour la recherche et dans l’UFR d’informatique d’AgroParisTech pour l’enseignement. Les activité d’enseignement s’inscrivent également dans la Graduate School « Informatique et Sciences du Numérique » de l’Université Paris-Saclay.

Mots-clés : apprentissage automatique, intelligence artificielle, intégration de connaissances, systèmes évolutifs.

Sujet :
Recherche :
Le/la Professeur(e) recruté(e) viendra renforcer l’équipe EKINOCS (comprenant 12 membres travaillant sur des méthodes d’apprentissage automatique, d’intégration de connaissances, de visualisation des données et d’optimisation) dans le domaine de l’apprentissage automatique. Les questions étudiées portent sur les sciences du vivant, de l’environnement et de l’agronomie dans le cadre de projets académiques et/ou industriels (ANR, Cifre, …). Par exemple : des questions sur l’environnement (e.g. suivi et interprétation de séries temporelles d’images satellitaires, analyse d’images par drones), le suivi en temps réel de chaînes logistiques ou de systèmes complexes (détection précoce et classification automatique d’anomalies et prise de décision en-ligne), l’analyse de processus de transformation, la nutrition (recherche des déterminants des comportements alimentaires et conception d’un coach numérique personnalisé pour modifier durablement les choix de l’utilisateur), l’analyse de biotopes en lien avec la santé, etc. Ces questions sont autant de stimulants pour développer des méthodes et techniques nouvelles d’apprentissage automatique (machine learning).

Enseignement :
Le/la Pr recruté(e) aura vocation à concevoir et dispenser des enseignements dans l’ensemble des formations relatives à l’informatique d’AgroParisTech, ce qui peut inclure des enseignements utilisant les technologies numériques.
Les enseignements à pouvoir prendre en charge sont au niveau du cycle ingénieur et en master :
• En première année : en tronc commun : algorithmique, programmation, bases de données, et en UEs optionnelles sur des méthodes informatiques particulières ou certains aspects des sciences des données, par exemple les techniques de visualisation des données.
• En deuxième année : dans des UEs optionnelles à la fois pour des enseignements relevant du département MMIP, mais aussi pour d’autres départements.
• En troisième année, en dominante IODAA qui est centrée sur les sciences des données et, potentiellement, dans les masters d’intelligence artificielle et de bio-informatique à l’Université de Paris-Saclay.
Le/la Professeur(e) pourra aussi participer à, et organiser, des modules d’école doctorale.

Profil du candidat :
Le/la Professeur(e) recruté(e) viendra renforcer l’équipe EKINOCS. Il/elle aura vocation à interagir avec des acteurs en particulier du monde des sciences du vivant, de l’environnement et de l’agronomie. Il/elle sera amené(e) à prendre rapidement en charge des encadrements de thèses / stages, et à développer de nouvelles activités collaboratives par le biais de projets structurants académiques et/ou industriels (ANR, Cifre…). Il/elle aura l’opportunité de participer aux activités de l’Institut DataIA de l’université Paris-Saclay et de la chaire H@rvest (agriculture numérique) et pourra initier des programmes nationaux ou internationaux.

Formation et compétences requises :
Le(a) candidat(e) devra être titulaire d’une Habilitation à Diriger des Recherches en Informatique et plus spécialement dans le domaine de l’intelligence artificielle relevant des sciences des données de façon à pouvoir orienter l’enseignement et la recherche dans cette discipline.
En termes de recherche, il/elle devra justifier d’une activité reconnue sur le plan académique aussi bien au niveau national qu’international. Une attention particulière portera sur son appétence à travailler dans les domaines des sciences du vivant et de l’environnement et à animer et conduire des projets de recherche.

Adresse d’emploi :
Lieu de travail : AgroParisTech, campus de l’Université Paris-Saclay.
Si vous souhaitez des informations complémentaires merci de contacter :
– pour le volet recherche : Antoine Cornuéjols (Responsable de l’équipe EKINOCS) : antoine.cornuejols@agroparistech.fr
– pour le volet enseignement : Liliana Ibanescu (Responsable de l’UFR d’informatique d’AgroParisTech) : liliana.ibanescu@agroparistech.fr

Document attaché : 202201031101_Poste-Pr-info-2022-AgroParisTech.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : CReSTIC
Durée : Poste statutaire
Contact : nicolas.passat@univ-reims.fr
Date limite de publication : 2022-04-30

Contexte :
Un poste de maître de conférences dans le domaine de l’image / intelligence artificielle pour la santé sera ouvert à l’Université de Reims Champagne-Ardenne au titre de la campagne de concours synchronisée 2022.

L’intégration se fera au sein du CReSTIC (https://crestic.univ-reims.fr) pour la recherche, et de l’IUT de Reims (Département Informatique) pour l’enseignement.

Mots clefs : analyse d’images ; machine learning ; analyse de données ; imagerie 3D ; données biologiques.

Sujet :
Le CReSTIC développe des activités de recherche en informatique en lien avec les problématiques médicales. En particulier, le CReSTIC collabore avec plusieurs services au sein de structures hospitalières (médecine nucléaire, radiologie, néonatologie, endocrinologie…) ainsi que des acteurs majeurs du monde industriel (construction d’imageurs, développement des logiciels libres…), et s’attache à développer des méthodes et outils numériques dédiés à l’extraction d’information à partir de données médicales (imagerie 3D, signaux électrophysiologiques, données biologiques, parcours patient…). Le/la candidat(e) recrutée viendra renforcer les équipes et chercheurs travaillant sur ces activités. Il/elle aura vocation à interagir avec des acteurs médicaux, ainsi que des chercheurs et enseignants-chercheurs en informatique mais aussi relevant de disciplines connexes à ces domaines (mathématiques, biologie…). Il/elle sera amené(e) à prendre rapidement en charge des encadrements de thèses / stages, et à développer de nouvelles activités collaboratives par le biais de projets structurants académiques et/ou industriels (ANR, Cifre…).

Profil du candidat :
Les compétences attendues du/de la candidat(e) relèvent de l’image (analyse, traitement), de l’intelligence artificielle (machine learning, analyse de données…) et de l’application de ces domaines dans un contexte médical.

Formation et compétences requises :
Les compétences attendues du/de la candidat(e) relèvent de l’image (analyse, traitement), de l’intelligence artificielle (machine learning, analyse de données…) et de l’application de ces domaines dans un contexte médical.

Adresse d’emploi :
Lieu de travail : Campus Moulin de la Housse, Reims.

Si vous souhaitez des informations complémentaires merci de contacter :
– pour le volet recherche : Nicolas Passat (directeur-adjoint du CReSTIC) : nicolas.passat@univ-reims.fr
– pour le volet enseignement : Jean-Michel Nourrit (chef du Département Informatique de l’IUT de Reims) : jm.nourrit@univ-reims.fr