The ImViA lab at University Burgundy (Dijon - France), together with Honda Research Institute (Japan), invites applications for postdoctoral research positions in deep learning for video analysis.

The postdoctoral researcher will work on improving existing deep learning models to estimate physiological signals from the video signal. Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate and other vital signs by analyzing subtle skin color variations using regular cameras (see [1] for an interesting review). More recently, end-to-end approaches based on deep learning have also been used. We will seek to extend existing work by improving current models, focusing on night vision applications. The candidate will take part in ongoing projects and possibly initiate new research within the team.

The postdoctoral researcher will work in Dijon - France in collaboration with researchers from the Honda Research Institute in Japan. This fellowship has a duration of 12 months with possibility of extension. As part of this postdoc, we can offer generous support for professional travel and research needs.

We are seeking a highly qualified and motivated candidate with a Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering, or a closely related field with a relevant scientific track record on significant computer vision conferences/journals as well as experience on deep learning techniques and frameworks.

Interested candidates should submit their CV, letter(s) of reference, and a brief research statement describing their background and research interests and how they align with the project emailed to Yannick Benezeth (yannick.benezeth@u-bourgogne. fr). The call will remain open until the position is filled. The postdoc contract will start as soon as possible.

University website: https://en.u-bourgogne.fr/

Lab website: https://imvia.u-bourgogne.fr/

Yannick Benezeth professional website:

https://sites.google.com/view/ybenezeth

https://scholar.google.fr/citations?user=JZ6tlZwAAAAJ

Open Research Position at Inria Lille – Nord Europe Research group: BONUS

Last annual activity report: https://www.inria.fr/en/inria-ecosystem

Description ----------- The INRIA research institute (https://www.inria.fr/en/inria-ecosystem) invites applications for multiple permanent researcher positions to start in the 2022-2023 academic year. High performance computing (HPC) and/or Machine Learningassisted big optimization is among its top priorities for the recruitment. This hot topic is a major focus of the BONUS research group, which is part of the INRIA Lille – Nord Europe research center located at Lille (North of France). Qualified applicants in Computer Science are invited to apply with the objective to join the BONUS research group as a permanent researcher.

Qualifications -------------- The BONUS team addresses big optimization problems (high-dimensional in decision variables and/or objectives, and/or with computationally expensive black-box functions) using mainly Machine Learning-assisted optimization and High-performance (parallel) optimization.

Candidates should hold a Ph.D. or equivalent degree in Computer Science or a related discipline. BONUS is interested in applicants having a strong background in at least one of its research lines: Machine Learning-assisted optimization or parallel optimization. Candidates with only HPC-related profile, highly motivated to adapt their activities to the context of big optimization using HPC in keeping with the BONUS research program, are also invited to apply.

Some benefits in addition to the salary --------------------------------------- − Subsidized meals,

− Partial reimbursement of public transport costs,

− Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.),

− Possibility of teleworking (after 6 months of employment) and flexible organization of working hours,

− Professional equipment available (videoconferencing, loan of computer equipment, etc.)

− Social, cultural and sports events and activities,

− Access to vocational training, − Social security coverage.

Application Instructions / Contact ----------------------------------- Applicants are asked to contact the leader of the BONUS team, Prof. Nouredine Melab at [email protected]. After this contact, if a candidate is encouraged to apply he/she will do it officially through the INRIA online application process that will be soon indicated here: https://www.inria.fr/en/talents

********************************************************************** OLA'2022 International Conference on Optimization and Learning (SCOPUS, Springer) 18-20 July 2022, Syracuse, Sicilia, Italy http://ola2022.sciencesconf.org ***********************************************************************

Prof. El-ghazali TALBI Polytech'Lille, University Lille - INRIA CRISTAL - CNRS

The Machine Learning team of the LITIS laboratory is still looking for a post-doctoral researcher or a research engineer, for a position in deep learning for sentiment analysis, funded by by the ANR CATCH project.

Title: Deep Learning for opinion mining in human testimonials related to industrial accident

Keywords: deep learning, NLP, sentiment analysis, twitter

Location: LITIS lab., University or Rouen Normandy, Rouen, France

Duration: 18-months, starting as soon as possible

Salary: ~2300€/month (before income tax), including social security coverage in line with French regulations

Please find the detailed description of the position attached to this email.

This position is still unfilled, so please feel free to forward to anyone who might be interested.

Best regards, -- Simon Bernard LITIS / NormaSTIC FR CNRS 3638 University of Rouen Normandy, France http://pagesperso.litislab.fr/ sbernard/

Post-doctoral or Research Engineer position Deep Learning for opinion mining in human testimonials related to industrial accident

Location : LITIS lab., University of Rouen Normandy, Rouen, France

Duration : 18-months, starting as soon as possible

Salary : ~2300€ / month (before income tax), including social security coverage in line with French regulations

The Machine Learning team at the LITIS laboratory, the computer science laboratory of the University of Rouen Normandy, is looking for a post-doctoral researcher or a research engineer on a 18-months contract, starting as soon as possible.

The position will be financed by the ANR research project CATCH (french acronym for "Automatic Understanding of Human Sensors Testimonials"), which involves the R&D center of the company Saagie1 , specialized in B2B DataOps solutions, Atmo Normandie2 , one of the approved French air quality monitoring associations and LITIS.

Keywords Deep learning – Natural Langage Processing – Sentiment Analysis / Opinion Mining

Scientific context The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take into account and automatically exploit the multitude of human testimonies related to an industrial accident and its consequences on the environment and health.

By involving the population in the collection and analysis of data, particularly through social networks, and by providing effective means for interpreting this data, the proposed solution should contribute to providing answers to the worrying problem of industrial accidents and their consequences.

The overall objective is first to draw up a precise cartography of the nuisances in order to follow the propagation and the evolution of the phenomena in time, and then to analyze and characterize the sentiment of the population and its evolution throughout the crisis.

To do so, we intend to exploit testimonials collected on the ODO platform3 of Atmo Normandie, which combines these testimonies with geographical information, in conjonction with data extracted from the micro-blogging platform Twitter.

Since these data are primarily texts, state-of-the-art approaches from the Natural Language Processing (NLP) field are favored, in particular, self-supervised deep learning methods such as Transformers4 that are known to be the most performant today for a wide range of NLP tasks5 .

Research goals The objective of this research work is twofold:

1. The automatic generation of a map of nuisances around the site of an industrial incident to monitor the propagation and the evolution of the phenomena over time.

2. The automatic recognition of the population's perception and its evolution throughout the crisis.

Related to these tasks, the post-doctoral researcher or the research engineer will be in charge of proposing and implementing solution for:

• extracting and linking twitter data with testimonials from the ODO dataset, which is fully labelled and associates textual testimonies with geographical data.

The interest in establishing this link is to be able to enrich the data from the ODO platform to refine the mapping of nuisances in real time.

This could be achieved for example, by using pseudo-labelling techniques6

or Constrative Representation Learning methods which have recently been applied to text data7 .

• detecting in all the testimonials collected from Twitter or from the ODO platform, the presence (or absence) of several pre-identified emotions (e.g. surprise, fear, anger, sadness, disgust, etc.),

several of which can be expressed at the same time.

This work will therefore involve being familiar with the state-of-the-art NLP deep learning methods and in particular with their applications to sentiment analysis and opinion mining tasks.

It will also require experience with the use and exploitation of data from Twitter in a data science context.

Application

The successful applicant will: • have a PhD or a Master's degree or an engineering degree in computer science or applied mathematics, with a specialization in machine learning or data mining.

• have strong programming skills and in-depth understanding of statistics and machine learning.

• have already worked with deep learning architecture dedicated to texts (RNNs, Transformers, etc.) and/or images (CNNs, FCNs, GANs).

Your application should include:

• curriculum vitae

• statement of past experiences with deep learning and/or NLP techniques

• representative published articles where applicable

• contact information for the referent PhD supervisor or last diploma's referent teachers

Contact

Application must be sent to :

• Simon BERNARD, University of Rouen Normandy, simon.bernar d @univ-rouen.fr

• Clément CHATELAIN, INSA Rouen Normandy, [email protected]

• Alexandre PAUCHET, INSA Rouen Normandy, [email protected]

Project managers: Rémi Régnier ([email protected]), Olivier Galibert ([email protected])

Duration: 18 months

Entitled : Post-doctoral fellow in the evaluation of AI systems on analog hardware

You will join a team of ten engineers and doctors regularly accompanied by post- doctoral students, doctoral students and interns, specializing in the assessment and qualification of systems artificial intelligence.

This team is historically recognized for its expertise in the evaluation of automatic information processing systems (language processing, image processing, etc.). In recent years, it has diversified in terms of areas application of its intelligence assessment expertise by dealing with subjects such as medical devices, collaborative industrial robots, autonomous vehicles, etc.

She capitalizes on the diverse and targeted know-how of its experts (NLP, imaging, robotics, etc.) in order to jointly find a satisfactory solution to the issue of evaluation and intelligent systems certification.

LNE is one of the three European partners of the AIR project of the Chist-Era program (2020- 2022, https://www.chistera.eu/projects/air) on the topic Analog Computing for Artificial Intelligence.

The project will focus on the use of radar technology for detection and tracking human vital signals (respiration, heart rate) for various applications such as monitoring of medical patients, intervention robots ...

In this context, LNE will have to put in place of new evaluation protocols for innovative analog-based AI systems and carry out a comprehensive evaluation campaign to promote this new product internationally type of hardware.

Missions:

As a post-doctoral fellow in the evaluation of AI systems on the AIR project, your field Priority intervention will be to materialize the evaluation campaign for this project.

It will pass by defining evaluation protocols, metrics, test benches, carrying out the campaign and comparison with the existing one.

The position will require you to work in close collaboration with the Finnish laboratories of the VTT (hardware designer) and Polish LUT (algorithm designer) for good progress of the project.

It will also ask you to know how to promote at the international level the project results.

You will cover the following missions:

 Design and validation of test protocols for radar technologies with embedded AI (both analog and digital media)

 Design and implementation of evaluation metrics

 Design of benchmarks for benchmarking

 Definition and design of test benches with the staff of the LNE test department

 Programming and conduct of evaluation campaigns

 Collaboration with European partners for the follow-up of the project

 Participation and organization in project meetings

 Publication and presentation of scientific results

Profile (training / school):

 BAC + 8 (preferably in information and communication sciences and technologies with a specialization in AI or hardware)

Skills and knowledge:

 Artificial intelligence

 Systems evaluation

 Electronics / electromagnetism

 C ++ / Python programming

 English

Context & research area The LIMOS works in collaboration with the Analgesia Institute, a French research foundation in the field of innovation and pain detection. Analgesia is developing an e-health mobile application to improve pain assessment and support chronic pain patients on a daily basis.

The data collected from the application need to be analyzed.

The main objective of this post-doctoral position is to develop new models and methods based on learning representation for time series fuzzy clustering with an application to biomedical data.

One first step will be to experiment the state of the art on learning representation methods for clustering [MCLC21, MZLC19, YDW+20].

The second step will be to use the new learned representation based on deep neural network architecture to enhance the clustering [MGL+18, YDZ+19]. The final goal is the implementation of unsupervised data mining tools allowing the discovery of groups of individuals with identical pain behaviors, regardless of their initial pathology.

Candidate profile

- PhD in computer science with a focus on machine learning or data mining.

- Good skills in data analysis and more particularly in supervised or unsupervised classification of time series.

- Strong publication record.

- Programming skills in python language.

Application deadline: December 6, 2021

Localisation : LIMOS, Campus universitaire des C ́ezeaux, Universit ́e Clermont Auvergne

Starting date: January 01, 2022 (20 month)

Salary: around 2600e gross monthly

Application

Applicants are invited to send a single pdf file containing a cover letter describing their research background and motivation, a detailed CV ( with a list of publications )and the contact details of up to two referees.

Contact

Violaine ANTOINE [email protected]

Issam Falih [email protected]

Hello We are currently looking for an engineer to participate in the development of a platform as part of the TIGA project. More information by following the link below:

https://www.universite-lyon. en/job-offers/engineer-it-it-development-within-labex-imu-tiga-project-action-14--246657.kjsp? RH=INTRANETUDL

Do not hesitate to spread the offer around you, thank you.

Kind regards — Julien Velcin Professor of Computer Science L3 IDS Manager

HuNIS Cluster Coordinator University of Lyon, Lyon 2, ERIC UR 3083 http://eric.univ-lyon2.fr/~jvelcin

Hello everyone, We are looking for a candidate to do a thesis in Rennes, at IRISA, in collaboration with LS2N, in Nantes.

Start date: January 2022 or as early as

possible in 2022 Topic: Combining educational resources through graph representation learning

Supervisors: Zoltan Miklos, Hoël Le Capitaine, Michael Foursov

Keywords: educational resources, knowledge graphs, graph representation learning, higher-order networks

Candidate profile : Master in Computer Science or equivalent (ranked in the first third); good Python programming skills; good foundations on semantic web technologies (RDF, OWL, SPARQL); good foundations in machine learning, good oral and written communication skills in English.

Financing over 3 years (Labex Cominlabs): Net salary ~1500€.

To apply: send your application to [email protected] with a detailed curriculum vitae, transcripts, a list of two references and your master's report in PDF format. Applications will be received until the position is filled.

The description of the subject : http://people.irisa.fr/Zoltan.Miklos/2021_PhD_position_at_IRISA.pdf

Thank you for disseminating this offer to your students.

Sincerely, Zoltan

Hello We are looking for a master's intern or PFE engineer interested in pursuing a thesis in September 2022 in the field of the exploitation of the Web of data for the generation of interpretable strategic knowledge.

To become familiar with the subject of the thesis, the trainee will have to extend a method of automatic construction of comparative tables from Wikidata that has been proposed in [1].

[1] Giacometti, A., Markhoff, B., & Soulet A., (2021) Comparison Table Generation from Knowledge Bases. In European Semantic Web Conference (pp. 179-194). Springer, Cham.

Duration of the internship: 6 months

Start date: February 01, 2022

Host unit: Laboratory of Fundamental and Applied Informatics of Tours (LIFAT), Antenna of Blois

Supervision: Arnaud Soulet and Béatrice Markhoff

Gratuity: 550 euros net per month.

Profile sought: · Year BAC+5 in computer science in progress

· good Skills in Java development, systems administration and database

· knowledge of the principles, technologies and resources of the Data Web (RDF, OWL, SPARQL, DBpedia, Wikidata)

· good oral and written communication skills

To apply, please send your CV, cover letter and transcripts from last year to [email protected] and beatrice.markhoff@univ-rounds. en.

Arnaud Soulet and Béatrice Markhoff (LIFAT, University of Tours antenna of Blois)

Savoie Mont Blanc University is recruiting a

Engineer - Computer Science, Statistics and Scientific Computing (M / F)

Recruitment open in priority to tenured agents level A _ BAP E

Contractual recruitment possible

Quotity: 100% At

LISTIC / POLYTECH ANNECY-CHAMBERY

Annecy site Position to be filled: 01/03/2022

SKILLS Knowledge: • Architecture of technical solutions, IT development, project management

• Research data management, Geographic Information Systems

• Written and oral expression, in French and in English

Know how : • Use of machine learning middleware

• Use of the Cloud and virtualization tools

• Usual computer development languages ​​(Python, Java, C, Shell ...)

• Installation and maintenance of operating systems (Linux, Windows)

Know-how : • Team management

• Autonomy, rigor, professional conscience

• Communication with management, laboratory members and other components or departments involved

• Taking into account the sustainability of the service provided (procedures, documentation)

Desired training (s) and professional experience (s):

Engineer, Master or Doctorate in Computer Science / Data Science

4. CONDITIONS OF EMPLOYMENT

• Holder according to statutory conditions

• Fixed-term contract of 7 months (January- July 2022 - renewable)

• Monthly gross salary with reference to the level A contractual index scale: from 2 155.57 (INM 460) - remuneration proposed to take into account diplomas and professional experience

• Annual leave entitlement: 2.5 days per month

• Subsidies possibilities: catering, public transport, extracurricular activities ...

• Taking into account the different disability situations

Recruitment procedure :

To apply, send a CV and a LETTER OF MOTIVATION electronically before December 6, 2021 to the address [email protected] (Human Resources Department), and with a copy to [email protected]

For questions relating to the position and missions of the position, you can contact Sébastien Monnet: [email protected]

Mines Paris, part of the PSL University, trains engineers capable of meeting the challenges of tomorrow.

As part of its strategic plan, the School aims to be a player in benchmark in the fields of innovation and entrepreneurship, energy transition and materials for more economical technologies, mathematics and digital engineering for the transformation of the industry, including health, while remaining faithful, since its inception in 1783, to its values ​​of solidarity and openness to society.

The positioning of the Robotics Center1 on AI and Machine Learning (ML) consists in algorithmic and experimental research on the adaptation of models and methods of cutting edge of the AI ​​/ ML domain to the needs of our key application sectors: autonomous vehicles and intelligent transport, industry of the future, and collaborative or mobile robotics.

Within the Robotics Center, the position will be attached to the Industrial Engineering team whose research interest relates to the digitization of environments linked to the management of operations and supply chain.

The team contributes to research and teaching, in particular through the Urban Logistics Chair2 and the Specialized Masters in Industrial Management and Logistics Systems3

Skills required for the position

Holder of a doctorate in computer science, automation, industrial engineering or science management.

The recruited person must have the following skills:

• Modelization ;

• Operational research ;

• Machine Learning.

Additional knowledge in one of the following areas would be appreciated:

• Decision theory;

• Operations management;

• Supply chain management.

Working environment

Robotics Center, MINES ParisTech, 60 boulevard Saint-Michel, 75006 Paris

Duration: 1 year renewable

Start: January 2022

Remuneration: between 2300 and 2800 euros gross per month depending on experience

Application file

The file is composed as follows:

• CV with a complete list of publications;

• a cover letter ;

• description of their research project;

• two letters of recommendation.

The file should be sent to Arthur Gaudron ([email protected]).

Keywords: architectures, neural networks, dynamic inference, self-adapting NoC, reliable NoC Technical and scientific context Deep learning algorithms such as convolutional neural networks (CNN, Convolutional Neural Networks) are widely used in many common applications such as recognition and classification of images.

These CNNs are made up of several layers of neurons that require several megabytes (or even Giga for very advanced models) of parameters for billions operations in a single inference pass [1], thus requiring large data movements.

Of many research works have proposed dedicated architectures improving the inference of CNNs, but few have proposed optimization work on the interconnection network. Indeed, the integration of networks efficient in computing architectures for AI poses major problems of energy consumption, service guarantee and communication costs.

The processing of CNNs must not only ensure a high parallelism for a high speed but also to optimize the movement of the data of the whole system to achieve high energy efficiency.

In addition, hardware network accelerators current neural systems are not optimized for next-generation applications, where workloads change in real time and data transfers are irregular. These dynamic behaviors are also increasing due to the implementation of new optimization techniques, such as pruning [2] to reduce the number of network parameters, to improve energy efficiency, and in particular on deep neural networks (DNN, Deep Neural Networks).

In this context dynamic applications, load balancing and resource sizing are becoming tasks difficult to anticipate and perform. In addition, congestion and blockages in data transfers can easily compromise the performance of the quality of service (QoS).

At last, technological developments lead to a greater sensitivity of architectures to disturbances of the environment and the aging of components, and in particular for the communications support which must therefore be made reliable.

In this context, the work of the thesis will aim to develop a network on chip (NoC, Network-on-Chip) dynamic and fault tolerant to support the implementation of scalable and flexible AI algorithms.

He will be based on optimized management of the data flow for low-power and efficient processing access to CNN parameters.

The major challenge of this thesis is therefore to ensure the self- adaptable in the interconnection network for modern AI architectures, while ensuring QoS and by being reliable.

To address all of these challenges simultaneously, the thesis will have to propose management techniques of the innovative network itself based on AI in order to be able to adapt to application changes and the appearance of faults and / or congestion within the network.

The self-adaptability of the network must be supported by hardware techniques (such as light monitoring mechanisms [3] and local dynamic reconfiguration of the architecture for an intelligent use of resources) and software (such as fault tolerance mechanisms [4] and optimization heuristics [5] in order to load balancing traffic and bypass congested or faulty areas of the network).

Those techniques can use hybrid methods [6] that provide offline learning to monitor workload and power consumption and online decision to initiate reassignment applications within the architecture.

Supervisors: Hana KRICHENE Chiara SANDIONIGI

Email: [email protected] Email: [email protected]

Thesis supervisor: Sébastien PILLEMENT University of Nantes, IETR, CNRS (UMR 6164)

Email: [email protected]

Qualifications: Master in computer science / electronics. Good knowledge of neural networks and on-board programming. Good analytical skills and experimentation will be greatly appreciated.

Application: Send your file to Hana KRICHENE including:

- A detailed CV,

- A cover letter,

- Notes and rank over the last 3 years,

- The names of 2 references who could recommend you

Contact: Hana KRICHENE CEA DRT / DSCIN / LECA Carnot Institute CEA List - CEA Saclay - Nano-INNOV Bat 862 - PC 172 F91191 Gif-Sur-Yvette Cedex Telephone: +33 1 69 08 36 37 Email: [email protected]

Start date: September 2022