In recent years, the issue of resource efficiency has also become increasingly important in con- struction engineering, as soil and rock account for more than 50% of mineral construction waste.
Tunnel projects play a special role in this regard, as large quantities are generated at specific times and places. Due to the high degree of mechanisation and the associated advantages in terms of construction performance and safety at work, almost the half of tunnels is built with Tunnel Boring Machines (TBM).
For documentation and control of the construction process, these are equipped with various sensor systems that provide comprehensive data sets. But in this area, modern data- driven processes have not yet found a wide application.
This 24-month post-doc position is funded by the French-German ANR project REMATCH.
The overall objective of this project is to use the data sets from TBMs and conceive novel machine learning-based models to enhance the recycling of the large quantities of tunnel excavation material. In this regard, an innovative real-time measurement system for material characterisation is to be developed which gives decision support on the question if soil is “usable” or “not usable” for other purposes and thus has to be disposed of in a landfill.
This system will base on several approaches using Computer Vision (CV) and state-of-the-art machine learning methods: firstly, on video data of excavated material on a conveyor belt, and secondly, on sensor data recorded simultaneously by the TBM.
Objectives:
The objectives of this post-doc position are to develop a computer vision system that analyses the videos captured from one or several cameras installed at the TBM and filming the excavated material on the conveyor belt.
In order to decide on the possible reuse of the material, different geophysical properties need to be estimated from visual features extracted in real-time from the video stream(s) coming from RGB cameras. This is challenging due to the mediocre acquisition conditions under low lighting and fast motion inducing some motion blur.
More specifically, after some preprocessing, a first step is to develop a machine learning solution based on appropriate CNN models that are trained for either classification and/or regression tasks in a supervised manner.
Different models should be developed and evaluated in terms of robustness and precision. To go further, novel innovative neural network-based architectures and weakly supervised learning schemes should be proposed to learn a latent representation that reflects the meaningful similarities of relevant soil characteristics.
Then, potentially other physical properties should be incorporated more explicitely into this semantic latent representation (either via a specific CNN, an autoencoder- type model and semi-supervised or contrastive learning depending on the amount and type of available annotation and the possibility to exploit the TBM sensor data for labelling).
The goal is to make the learnt features and models more explainable. After evaluating these models, a final approach would be to incorporate TBM sensor data in a combined and multi-modal architecture.
Environment:
The position is in the IMAGINE team of the LIRIS laboratory in Lyon (Campus La Doua) under the supervision of Catherine Pothier and Stefan Duffner. The funding is for 24 months. Additional teaching activities may be conducted at INSA Lyon if the candidate desires to.
Contact:
Stefan Duffner [email protected] Catherine Pothier [email protected]
Type of contract: CDD
Contract duration: 1 year
Assignment: Participation in a scientific project in the medical field.
The objective of the project is the construction of a longitudinal cohort of subjects with diabetes and monitored in one of the AP-HP hospitals (Assistance Publique Hôpitaux de Paris). The data included in this cohort are taken from electronic medical records available in the AP-HP health data warehouse (EDS). The project is collaborative (APHP doctors, INRIA mathematicians, INSERM/University researchers and DSI AP-HP).
Part of the mission of the position is to create a process automated data extraction from medical records electronics (base i2b2 and OMOP) thanks to NLP tools (Natural Language Processing).
The other part of the mission is to develop algorithms for identifying pathologies and care pathway within the cohort in order to identify determinants of the risk of medical events and to assess the effectiveness different types of care (course of care, treatments)
The mission will be conducted in close collaboration with physician-researchers (APHP, INSERM, Paris Cité University and Sorbonne University), mathematicians from INRIA (SODA team) and the teams ISD of EDS APH-HP.
Required Skills : - Knowledge of Python programming
- Mastery of NLP tools
- Knowledge of the medical field would be a plus (not mandatory)
Duration : 15 months (potentially extensible to 18 months) - Expected starting date January 2023
Team : SyCoSMA at LIRIS-CNRS (UMR 5205), University Claude Bernard-Lyon 1
Project : PepperMint funded by ASLAN Labex
Partners: LIRIS (SyCoSMA, SAARA Teams), ICAR (InSitu Team), University of Oulu-Finland (GenZ)
Supervision: Pr. Salima Hassas , Dr. Mathieu Lefort
Context PepperMint (Interacting with Pepper: mutual learning of turn-taking practices in HRI) is funded by the ASLAN Labex.
It proposes an exploratory study of embodied turn-taking practices in task-related Human-Robot Interaction (HRI) to improve the social abilities of robots and make HRI more natural to humans.
The project initiates a cooperation between researchers in AI (Artificial Intelligence) (LIRIS) and CA (Conversation Analysis) (ICAR and GenZ Oulu - Finland). It investigates if and how CA findings on natural occurring interaction can be used to develop innovative and effective AI models for HRI.
The project is grounded in a detailed multimodal analysis of turn-taking in naturally occurring HRI, putting forward the emergence of turn allocation as complex sequential and multimodal practices.
The project is built upon existing works on AI/ML (Machine Learning) algorithms of the state of the art to program an application for reception and orientation of people in an university library. Previously, we recorded human-robot interactions based on a first ad-hoc version of the robot with state of the art algorithms and ad-hoc turn-taking practices. These data are used in CA studies to identify successful interactions.
The goal of this post doc is to use this annotated dataset for machine learning methods to propose a new AI model for HRI, coupling developmental learning and CA findings.
The detailed missions of the Post Doc will be: – To review the state of the art algorithms for Turn-Taking.
– To collaborate with another Post-Doc in the field of Conversation Analysis, to clean and prepare the annotated data that will be produced by the CA researchers, and create new algorithms for ML according to CA findings.
– To develop a new version of the HRI application with new ML (oriented towards Developmental Learning) algorithms to improve turn-taking practices in HRI.
– To contribute to the (scientific) communication activities of the PepperMint Project.
Required Skills:
We are looking for a Post Doc to join our project team composed of researchers, engineers and practitioners in the field of AI / Social Robotics and CA.
The ideal candidate will have the following skills and background:
– Strong Expertise/Experience/Background in AI and Machine Learning
– Good development/programming skills in Object Oriented Programming (e.g. Java, C++, Python)
– Fluent or good level in written English
– Open mindness, teamwork, autonomy and capacity to interact with other disciplines like social sciences.
– Interest in interdisciplinary research
– Knowledge in Social Robotics (Human Robot Interaction) would be a plus
Application
Applications should include a detailed curriculum vitae, a statement of interests and two reference contacts.
Applications and letters should be sent via electronic mail to: [email protected];
Deadline for application: October 20th, 2022
(Please note that 2 to 3 months will be taken by the administration for the hiring procedure)
Working environment:
The recruited candidate will be employed by: CNRS, Université Claude Bernard-Lyon 1, at LIRIS-CNRS Laboratory, located at Nautibus building, Lyon.
Duration of the contract: (at least) 12 months
Date of hiring : December 2022
Contacts/advisors: Frédéric LERASLE (Professor in CV, LAAS-CNRS, [email protected]), Cyril BRIAND (Professor in Operational Research, LAAS-CNRS, [email protected])
More details and application here : https://emploi.cnrs.fr/Offres/CDD/UPR8001-FRELER-003/Default.aspx
MISSIONS The postdoc recruited on a collaborative project between the RAP (https://www.laas.fr/public/fr/rap) and ROC (https://www.laas.fr/public/fr/roc) research groups at LAAS-CNRS of Toulouse will study the existing literature combining Operational Research (OR) and machine learning methods in the field of computer vision, in particular those aiming at applications of detection, localization and reidentification of targets in video streams. In a first step, he/she will pursue the work undertaken in the field of person reidentification, which will allow to consolidate these skills and to quickly valorize first results in conferences of Operational Research and Computer Vision. In a second phase, he/she will address the problem of posture estimation by taking inspiration from the methods already proposed in the literature. Finally, he/she will work on integrating all these techniques in order to propose a complete chain of person detection, re-identification and posture tracking in a multi-camera network. The thesis project will be conducted according to an agile approach aiming at producing more and more successful and realistic context experiments by exploiting the camera network already deployed in the ADREAM building (https://www.laas.fr/public/fr/le-projet-adream) of the LAAS-CNRS and thus propose an advanced proof of concept of the approach, dedicated to the visitors of the laboratory.
ACTIVITIES
Contributions to vision & machine learning state-of-the-art techniques
Use of combinatorial optimization solvers
Implementation of algorithms in C/C++/Python
REQUIRED SKILLS AND QUALITIES This offer is specifically dedicated to PhD fellow in Computer Vision with good theoretical background in combinatorial optimization and machine learning.
Other skills: - Autonomy, teamwork
- Tools for vision and machine learning: OpenCV, neural network libraries and architectures
2 post-doc positions in Socially Acceptable Extended Reality Systems, King’s College London, UK
The post-doctoral researchers will join a team at King’s College London in the context of the Horizon Europe Framework Programme (HORIZON) project “SERMAS: Socially-acceptable Extended Reality Systems and Models”. The team at King's comprises Dr Oya Celiktutan (Department of Engineering), and Prof. Luca Viganò (Department of Informatics). This is an exciting opportunity to join our team and participate in a new area of research, and in the process collaborate with our SERMAS partners in Italy, Germany, Ireland, and Switzerland.
The revolutionary opportunities opened by eXtended Reality (XR) technologies will only materialize if concepts, techniques, and tools are provisioned to ensure the social acceptance of XR systems. For that, we need XR systems that are not just innovative and functionally complex, but also provide an experience that: satisfies the goals and needs of the user, is in compliance with the social context in which the system is being used, and is transparent, safe, secure, explainable and is trusted by the user. However, current generations of XR systems fail to provide the XR experience they were envisioned for since state-of-the-art models and technologies of XR systems fail to ensure full-fledged social acceptance. A truly XR experience requires a major paradigm shift in the way XR systems are designed, implemented, deployed and consumed. The SERMAS project will develop innovative, formal and systematic methodologies and technologies to model, develop, analyze, test and user-study socially- acceptable XR systems.
### Position 1
Research Associate in Socially Intelligent Robotic Systems
Duration: 24 months, full-time
Start date: flexible, latest in May
Link: https://jobs.kcl.ac.uk/gb/en/job/056585/%E2%80%8B%E2%80%8BPost-doctoral-Researcher-in-Socially-Intelligent-Robotic-Systems%E2%80%8B
The SERMAS project aims to develop the next generation of interactive agents that can cooperate with and assist humans in a socially acceptable manner, with higher levels of autonomy. This requires novel approaches for multilevel analyses of human behaviour, for example, from their gestures to their emotions and personality, and multimodal synthesis of such behaviours via virtual agents/robots for system adaptation and personalization. The developed agents will be evaluated in real-world scenarios taking roles as receptionist, training coach, and office assistant. The role holder will be highly experienced in machine learning applied to human behaviour analysis and synthesis via embodied agents and human-robot interaction, as well as have a strong publication record in related areas. The research will have a substantial multidisciplinary ambition, but a PhD in computer science, mathematics or engineering is essential.
# Key responsibilities
The successful candidate is expected to: 1. shape and engage in advanced multi-disciplinary research in collaboration with the SERMAS partners
2. maintain an outstanding track record of published research at a level of international excellence and
3. lead activities promoting research impact
# Skills, knowledge, and experience Essential criteria
1. PhD degree in Engineering, Computer Science, Robotics, or a related field. [or near completion]
2. Strong background in machine learning, with specialisation in one or more of the following areas: human-robot interaction, extended reality systems, human behaviour analysis and synthesis, human-centric computer vision, and multimodal learning.
3. Excellent publication record in high-quality journals and/or conference proceedings.
4. Excellent programming skills, particularly, Python and/or C/C++. Hands-on experience with deep learning libraries (e.g., PyTorch) and/or physical robots will be a plus.
5. Ability to organise and prioritise work to meet deadlines with minimal supervision.
6. Excellent interpersonal/teamwork skills.
Desirable criteria 1. Experience in devising and developing novel machine learning algorithms.
2. Excellent mathematics skills.
3. Familiarity with managing project work packages and writing deliverables.
### Position 2 Research Associate in Formal and Automated Security Analysis of Extended Reality Systems
Duration: 33 months, full-time
Start date: ASAP
Link: https://jobs.kcl.ac.uk/gb/en/job/056268/Post-doctoral-Researcher
To provide all stakeholders of Extended Reality systems with the different levels of certified assurance that they require, the SERMAS Toolkit will rely on the development and application of formal methods and tools for compositional security assessment. Formal methods will also us to formalise and integrate explanations in the different phases of system development, from design to execution. It will be necessary to identify reasonable trade-offs that will allow to adapt explanations so that they can be verified and accepted by the different stakeholders, while at the same time guaranteeing the validity and formality of the explanations. The role holder will be highly experienced in cybersecurity and formal methods, as well as have a strong publication record in related areas. The research will have a substantial multidisciplinary ambition, but a PhD in computer science, mathematics or engineering is essential.
# Key responsibilities The successful candidate is expected to:
1. shape and engage in advanced multi-disciplinary research in collaboration with the SERMAS partners
2. maintain an outstanding track record of published research at a level of international excellence and
3. lead activities promoting research impact
# Skills, knowledge, and experience
Essential criteria
1. PhD in Computer Science, Engineering, Extended Reality systems, cybersecurity, formal methods, artificial intelligence or related field [or near completion]
2. Solid background in formal methods
3. Solid background in cybersecurity
4. Enthusiasm to work collaboratively with partners from multiple disciplines across the international project.
Desirable criteria
1. Solid background in Extended Reality
2. Excellent publication record in high-quality journals and/or conference proceedings.
3. Knowledge of various related subdisciplines including privacy, robotics, human-computer interaction, machine learning, user studies
4. Familiarity with aspects of ethics of extended/augmented reality systems
5. Ability to organise and prioritise work to meet deadlines with minimal supervision.
6. Familiarity with managing project work packages and writing deliverables
### Further information
The selection process will include a brief pre-recorded video presentation and an online panel interview. Interviews are scheduled to be held in November. Detailed scheduling will be confirmed once shortlisting has taken place.
### About King’s College London
King’s College London is located at the heart of London. Our staff and students come from all over the world. The Department of Engineering and Department of Informatics are proud of its friendly and inclusive culture and are committed to ensuring an inclusive interview process. They will reimburse up to £250 towards any additional care costs (for a dependent child or adult) incurred because of attending an interview for this position.
For further information about the Department of Engineering at King’s, please see https://www.kcl.ac.uk/engineering/research. The candidate will be a member of the Social AI & Robotics Laboratory, please see https://www.kcl.ac.uk/news/helping-robots-to-understand-people-the-work-of-the-social-ai-and-robotics-lab.
For further information about the Department of Informatics at King’s, please see https://nms.kcl.ac.uk/luc.moreau/informatics/overview.pdf. The candidate will be a member of the Cybersecurity Group and of the Security Hub, please see https://www.kcl.ac.uk/research/cys and https://www.kcl.ac.uk/research/security-informatics.
Colleagues, Texas Robotics at the University of Texas at Austin invites applications for tenure-track faculty positions. Outstanding candidates in all areas of Robotics will be considered.
Tenure-track positions require a Ph.D. or equivalent degree in a relevant area at the time of employment. Successful candidates are expected to pursue an active research program, to teach both graduate and undergraduate courses, and to supervise students in research. The University is fully committed to building a diverse faculty and we are interested in candidates who will contribute to diversity and equal opportunity in higher education through their teaching, research, and service.
The current robotics faculty includes representatives from several departments who work to advance fundamental scientific and engineering knowledge in areas such as intelligent navigation and manipulation, robot learning, and multi-robot systems, as well as on numerous applications including social, rehabilitation, surgery, autonomous vehicles, drilling, manufacturing, space, nuclear, and defense. Full detail on our program are available at http://robotics.utexas.edu. The group recently moved into a custom-renovated historic building at the center of campus.
To be considered for a position, please apply to one or more of the following three departments:
- Computer Science: https://www.cs.utexas.edu/faculty/recruiting - Electrical and Computer Engineering: https://www.ece.utexas.edu/jobs - Mechanical Engineering: http://apply.interfolio.com/113653
In addition, to ensure that your application is connected with the robotics search, please send your cover letter and CV to
along with an indication of which department(s) you applied to.
Complete applications to any of the above departments that meet the corresponding deadline will be considered. Robotics applications will be reviewed as they are received. Applicants are encouraged to apply as soon as possible for full consideration.
IRISA-CNRS (Rennes, France) offers a post-doc position (18 month contract) on NLP. The recruited researchers will work on symbolic/deep learning hybridization, constrained text generation in the framework of an ASTRID/AID funded project about disinformation.
Details and application: https://emploi.cnrs.fr/Offres/CDD/UMR6074-VINCLA-007/Default.aspx?lang=EN
The application may be reviewed by the Agence Innovation Defense.
Contact: [email protected]
We are actively looking for an engineer in charge of operations for the LNE's Artificial Intelligence and Cybersecurity Evaluation department:
https://www.lne.fr/fr/offre-emploi/ingenieur-en-charge-operations-departement-evaluation-intelligence-artificielle-0
The successful candidate will join a fast-growing team specializing in the evaluation of AI systems and working in many fields (TAL, image processing, smart medical devices, autonomous mobility systems, agricultural robots, cobots, etc.).
I am at your disposal for any exchange on this offer.
Thank you in advance for your applications and your shares, see you soon!
Guillaume AVRIN, PhD Head of the Artificial Intelligence Evaluation Department Head of cybersecurity testing activities
Directorate of testing and certification
Keywords: Smart Contracts, Web3 architectures, Security, Reliability, Formal Methods, Domain-Specific Languages.
Context: Considered as large, decentralized data ledgers that are always available, immutable and replicated, Blockchain technologies allow untrusted users to reach agreements on verifiable data as well as exchange digital assets without third-party intermediaries. In other words, these technologies represent a legitimate disruptor to many business areas such as payments, cybersecurity, health, and logistics, etc.
In the context of the so-called programmable blockchains, smart contracts are designed to directly and automatically control the interactions and the behavior of digital assets. For that purpose, the business logic and the underlying conditions must be first defined and implemented, before deploying them to the blockchain.
Harnessing blockchains to exchange financial and digital assets also induces safety and security needs. Many technical and financial risks are indeed inherent to the use of smart contracts. As computer algorithms, with all that this may entail, smart contracts may contain functional and security vulnerabilities.
The adoption of smart contracts in the context of Web3 - the next generation of internet - requires a very high level of trust.
Therefore, ensuring a proper and secure functioning of smart contracts implies the use of reliable security techniques based on solid mathematical foundations. This thesis proposes to tackle these by defining a formal approach supported by automatic reasoning tools (i.e., provers, model-checkers, and constraint solvers). The goal is to neutralize all risks of error throughout the smart contracts implementation process.
As part of its mission to democratize the use of smart contracts and Web3 architectures, FeverTokens places the use of formal methods at the heart of its security policy. However, so far, there are too few works exploring formal methods in the context of smart contracts, especially ones with successful industrial applications.
At this time, research works have been limited to feasibility studies and theoretical concepts without really addressing the scale-up and production constraints.
In this regard, the objectives of this thesis include the definition of a tool-supported approach that enables building safe-by-construction smart contracts. We will particularly focus on the design of a Domain Specific Language (DSL) that would permit to represent and consider both smart contracts and their functional/safety properties. Therefore, tested and/or proved transformation mechanisms will be proposed.
Finally, this thesis aims at exploring and extending the Meeduse toolset developed by the VASCO, LIG team. Meeduse is an IDE for the formal design of DSL, it allows proving the correctness of a DSL using the B formal method. It has been successfully applied to several case studies and was recently awarded two prizes at the TTC’19 challenge (Transformation Tool Contest): Best Verification Award and Audience Award.
Environment:
- Funding: the thesis will be funded under the CIFRE arrangement - Principal Hosting laboratory: SAMOVAR (Télécom SudParis) - Hosting company: FeverTokens - Partner laboratories/institutions: LIG (Grenoble INP), Faculté des Sciences de Rabat (Université Mohammed V)
- Location:
the thesis will take place mainly at FeverTokens office in Paris, as well as at the SAMOVAR laboratory, Télécom SudParis, Evry. Some travels to the LIG laboratory, Grenoble, are also expected
- Duration:
3 years, starting as soon as possible
Profile and skills:
- A master's degree, engineering diploma or equivalent, ideally in computer science/ automation - Good level in mathematics (i.e., logic, set theory, formal methods etc.) - Proven ability in algorithms and programming - Knowledge about blockchain and smart contracts is appreciated - Fluent in English - Sense of initiative, autonomy and ability to work in a team
Application:
Applications are to be sent by email to: - Pr. Amel Mammar [email protected] - Dr. Akram Idani [email protected] - Dr. Zakaryae Boudi [email protected] - Dr. Abderrahim Ait Wakrime [email protected]
They should include:
- A detailed CV - A copy of all diplomas - A copy of all post-bac transcripts - A motivation letter - Recommendation letter(s)
I would like to share a proposal for an M2 internship around deep reinforcement learning for game theory, to be filled from February 2023 at INRAE Toulouse.
Please find the detailed description of the offer in the attached pdf as well as on the website:
https://miat.inrae.fr/site/Emplois
This announcement is aimed at AI students interested in (deep) reinforcement learning and multi-agent planning (based on game theory).
In the process, a funded thesis (PRCI ANR-DFG) will begin in September, on the definition of original DeepRL algorithms for stochastic games. If the M2 trainee wishes, and if he has the required skills (the thesis is more theoretical than the internship), he can a priori continue in the thesis.
Context INTESCIA[2] is a French software editor that provides Business Intelligence (BI) solutions that embed predictive solutions for business opportunities based on Artificial Intelligence to validate, link and interpret the millions of data (sometimes source of anxiety) in order to allow company management to see more clearly and make the right decisions. Founded in April 2023, INTESCIA GROUP aims at being a major actor of the Data Economy. Its main activity is the management and the enrichment of the data so as to provide companies with efficient and cutting-edge business tools and services that are in adequacy with their expectations.
The Lab-STICC, with its double affiliation to the INS2I and INSIS institutes of the CNRS, is a research unit historically recognized in Brittany and in France in the field of ICTS. It has a proven capacity to cover a broad scientific spectrum around digital sciences, and in particular with this ability to address various disciplinary fields (Information Theory, Waves & Materials, Embedded Electronics and Computing, Data Sciences, Communication and Signal Detection, Human-Machine Interfaces,...) following multiple themes/application sectors: maritime environment, communicating objects, defense, space, health, security, robotics...
The MOTEL « MOdels and Tools for Enhanced Learning » team takes part of the research community EIAH (in French, Environnements Informatiques pour l’Apprentissage Humain”) - Computing Environments for Human Learning. MOTEL works on several subjects that contribute with computing human-centred tools, methods and models for Education using an experimental approach.
This PhD offer takes part in a collaborative project between INTESCIA and the MOTEL research team. The goal of this collaboration is to conceive novel analytic and predictive functionalities at the intersection between AI and BI.
Scientific issues DoubleTrade is an ultimate software solution to the monitoring of public sector tender that helps their French and foreign customers find the best professional opportunities.
A crucial issue for INTESCIA is to integrate cutting-edge analytic and predictive functionalities in the DoubleTrade solution that are both new and pragmatic. A possible direction for the new-generation of BI solutions is to provide users with concise and interpretable views of the current offers and their evolution. But most of all, the added value of such solutions lies in their capacity of identifying and recommending the most appropriate offers for a given company, and in fine to explain the reasons of each recommendation.
To reach this goal, four main scientific issues have to be addressed during the proposed PhD:
1. To define efficient algorithms to summarize the available offers [Smits et al., 2018] as well as comparison measures between such summaries. Comparisons should emphasize on the emerging trends [Dong and Li, 1999].
2. To enrich an a priori-defined corporate model (as a knowledge graph for instance) [Ji et al., 2021]) with knowledge automatically extracted from the data that model links between invitation to tender, actors (company, public services, decision makers), etc.
3. To conceive a recommendation system of invitations to tender guided by the corporate model [Guo et al., 2020] completed with explanation strategies that point out the reason of the recommendation. This last functionality will rely on recent work about explainable AI et especially the generation of appropriate contrastive explanations between relevant and irrelevant offers [Došilović et al, 2018].
4. To propose and develop novel dashboards that combine ``classical” views about static metrics, data summaries and recommendations of relevant offers associated with explanations about their provenance.
PhD organization The PhD student will work in the INTESCIA building (Issy-les-Moulineaux) or at the Lab-STICC (IMT Atlantique - Brest) with possibilities to work remote. Monthly meetings will regroup all the members (INTESCIA + Lab STICC) involved in the project.
Required training and skills The PhD candidate should possess a Master degree or be an engineer in computer science. In addition to be able to work in team and to be strongly interested in scientific research, we expect from the candidate to been trained on data science and machine learning. Skills in BI are very welcome.
How to apply? A CV and a motivation letter have to be sent to Grégory SMITS ([email protected]). The offer is available until an adequate candidate is found.
Postdoctoral opportunity at the CNRS Bioinformatics and Computational Biology lab in Bordeaux, France
** Position: 24-month postdoctoral fellowship
** Starting date: January 2022
EOSC4Cancer research and innovation action funded by European Commission, aims to connect a set of interoperable nodes, e.g. European Cancer Centres, Research Infrastructures, Medical Centres, that provide access to FAIRified cancer-related data within (a) trusted users environment(s). The selection of data and their connectivity will be driven by key use-cases that demonstrate the value at all stages along the cancer patient journey, and served in the visualization and analysis environments they are familiar with.
The postdoctoral researcher will specifically contribute to the advancement of cancer data analysis portals accross Europe. The work will be based on existing computational methods served in the form of software containers interoperable in Virtual Research Environments (VRE) and workflow managers (e.g. Galaxy : https://usegalaxy.eu). The use of community adopted platforms, cBioPortal (https://www.cbioportal.org) and the Clinical Decision Support Systems (e.g. MTBP, https://mtbp.org/ExamplePublic.php or PCGR https://github.com/sigven/pcgr), will make the operations of the underlying software layer transparent to the users.
Depending on the profile of the applicant, development of AI methods for integration of digital pathology images and clinical data can be envisioned.
The postdoctoral researcher will work in the “Computational Biology and Bioinformatics” team (IBGC, CNRS, Bordeaux - France) under the supervision of Macha Nikolski. Strong collaboration with other teams within the EOSC4Cancer project is expected, in particular with the team of Pr. Eivind Hovig, University of Oslo.
Qualifications: Ph.D. degree in Bioinformatics, Computer Science, Biostatistics, Applied Mathematics or related discipline.
Learn more about the offer: See the offer on the CNRS website: https://emploi.cnrs.fr/Offres/CDD/UMR5095-KILAUD-002/Default.aspx?lang=EN
Contacts : • Macha Nikolski [email protected] • Slim Karar [email protected]
A postdoc offer for a young researcher is proposed at the Ecole des Mines de Saint-Etienne, LIMOS (UMR 6158).
Topic : Data-Driven Falls Risk Prediction
Location : Engineering and Health Center, Mines Saint-Etienne, Computer Science, Modeling and Systems Optimization Laboratory (LIMOS UMR 6158)
Keywords : fall prediction, machine learning, data mining, direct industrial partnerships
To apply : https://institutminestelecom.recruitee.com/o/postdoctorante-postdoctorant-prevision-du-risque-de-chute-axee-sur-les-donnees
Do not hesitate to contact us for any clarification, or to distribute this offer to anyone who may be interested.
Keywords: multispectral imaging, remote sensing, IoT, image processing, deep learning, data fusion, digital agriculture.
Context: crop diseases cause economic losses, reduces quality and yields, and impacts negatively the environment when intensive chemical products are used for treatment.
The detection of crop diseases is therefore a major challenge for agriculture, but also for the economy and the environment. Modern technologies, such as IoT, drones, remote sensing, big data and artificial intelligence, integrated by information and communication technologies, have opened a new era for digital agriculture. Indeed, these technologies offer enormous potential to solve challenging problems such as early detection and management of diseases.
The state of the art shows a trend towards their widespread implementation. On the other hand, these advances have raised many challenges, such as the processing and analysis of heterogeneous data, the reliability of models and their generalization, etc.
Objective: crop disease monitoring can be performed using environmental and vegetation cover data, obtained from multispectral cameras, and IoT equipment.
Promising approaches have been proposed, in recent years, using deep learning (convolutional networks, recurrent networks, transformers, ...) and data from different types of sensors. However, the heterogeneous data (sensors: image, weather, etc.) weakly labeled, make it difficult to build effective models.
The aim is to develop methods based on deep network principles using recent paradigms such as self-supervised learning, attention mechanism, ... to detect and predict grapevine diseases.
Profile: PhD degree with experience in machine learning for analysis of images and data with different modalities.
Duration: ~18 months
Location: INSA CVL, 88 Boulevard Lahitolle, 18022 Bourges.
Applications: CV to be sent to: [email protected] ; [email protected]
Please find an internship offer for a Master 2 student or in the 3rd year of engineering school. In the process, a funded thesis will be able to begin inSeptember 2023in the continuity of the subject of M2.
title : Reinforcement learning techniques for Multi-agent path finding with imperfect information
supervisors :JillesDibangoye, Ocan Sankur, François Schwarzentruber
duration : 6 months
place : IRISA, Rennes
description :https://epirl.irisa.fr/files/ 2022/10/m2proposal_rennes.pdf
In the following thesis, we will try to make the link between reinforcement learning and epistemic logic (a modal logic to express properties such as "an agent knows that another agent does not know that..."), in order to explain the decisions made by agents in an imperfect information framework.
This ad is for computer science students with an interest in reinforcement learning and logic.
Collaboration LAAS-CNRS & ANITI / AIRBUS
Location : Toulouse
This thesis aims to increase the autonomy of avionics systems, such as being able to operate with a single pilot. Research will focus on anomaly detection and sensor diagnostics as well as data fusion to deliver correct information to the pilot. Indeed, failures or particular atmospheric phenomena can affect them, causing a sharp decrease in pilot assistance. A generic solution that can be adapted to each aircraft (MSN-centric) will be sought.
The first application will focus on sensors measuring air parameters (incidence, speed, etc.) and will highlight the gain in accuracy induced by the coupling of knowledge-based estimation methods and AI learning methods, by demonstrating the ability to detect anomalies related to frost shapes. The search for simplicity and readability will always be required to ensure an industrially deployable solution. The work will benefit from an industrial environment allowing rapid prototyping and comparison with existing algorithms thanks to the validation and verification tools made available, already developed as part of previous work on data fusion.
The thesis will be carried out in a Cifre context, associating LAAS-CNRS, the ANITI Institute and AIRBUS.
Academic contact: L. Travé-Massuyès ([email protected]) and Carine Jauberthie ([email protected])
Industrial contact: G. Alcalay ([email protected]) and P. Goupil ([email protected])
The MIDI (Multimedia Indexing & Data Integration) Team, ETIS UMR 8051 Laboratory, CY Cergy Paris University, ENSEA, CNRS (France) and the Institute for Infocomm Research, A*STAR (Singapore) launch a call for applications for a doctoral position in "Representation learning for multimodal data".
The PhD student will be involved in an international collaboration research between CY Cergy Paris University (France) and Institute for Infocom Research A*STAR (Singapore) spending 18 research months at ETIS Laboratory in France and 18 research months at A*STAR in Singapore.
Beginning of the Thesis: Ass soon as possible
Project description The performance of machine learning algorithms is largely determined by data representation, which is due to the fact that different representations may entangle and hide distinct explanatory aspects of variation behind the data to varying degrees. The problem using these types of representation learning on heterogeneous multimodal data, including time series, text, images, etc. present methodological issues in the modelling and learning from such complex data. This challenge renders the supervision process almost impracticable and very uncertain given the unknown dynamical nature of the observed systems.
We want through this thesis to explore the unsupervised topological learning of multimodal data presenting a complex structure allowing to learn their representations. We are particularly interested in heterogeneous data whose representation may have been informed in different ways: expert representation which may be complex (for example: dynamic multi-graphs which may possibly have different topologies for each observation of the dataset and for every moment) or automatically learned representation (for example: embedding in a high-dimensional space with dense vectors and potential correlations between the components), images, signals.
The topological learning models based on neural networks and probabilistic models will be analyzed in this project and the expected results are proposal of representation learning approaches able to learn the topological information from multi-modal data by increasing the reconstruction error.
Co-directors of the Thesis: Nistor Grozavu, Full Professor ETIS UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France Web : http://www.grozavu.fr
Xiaoli LI, Full Professor Institute for Infocomm Research, A*STAR, Singapore. Web:https://personal.ntu.edu. sg/xlli/
Shared ARAP scheme funding CYU (France) – A*STAR (Singapore)
Salary: 18 months CYU contract (legal doctoral salary, around 1500-1600€/month) + 18 months A*STAR contract (S$ 2700/month)
Running cost: 1000 €/year
Round trip to the other country: 600 - 1000 €
Per Diem in the other country: 100€
Intl conferences (at least 3): 2000€ including flight + registration + housing * 3
Affectation: ETIS UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France and Institute for Infocomm Research, A*STAR, Singapore. Web:https://personal.ntu.edu. sg/xlli/
To apply: Complete application with CV, recommendation(s) letter(s), academic results to be sent to: Nistor GROZAVU
email : [email protected]
We have an open post-doc position (1 year possibly renewable) described below or in this link:
https://www.linkedin.com/posts/vincentlemaire_post-doc-methods-for-detecting-erroneous-activity-6971436264935845889-2OBS/
To apply it's here: https://orange.jobs/jobs/v3/offers/117130?lang=fr
Title: Method for detecting erroneous labels
Global context and problematic topic: The field of low-supervised learning (WSL) has recently seen a resurgence in popularity, with many articles dealing with different types of "supervision defects", namely: poor quality, non-adaptability and insufficient labels. When it comes to quality, label noise can be of different types, including completely random, random, or even non-random. Label noise affects quality trained models. Detecting label noise results in a better model. However, we do not have a clear view of the state of the art methods capable of detecting instances that have a wrong label.
Scientific objective – results and obstacles to be resolved: The purpose of the postdoc is to review the methods of the literature. One of the objectives will be to publish this review and/or a white paper. The second objective could be to develop new Methods suitable for bi-quality learning and (i) have a tool for detecting erroneous labels and/or automate data cleaning and (ii) be able to learn with clean data + data with wrong labels.
The main challenges will be (i) to identify the best state-of-the-art methods (strengths, weaknesses, maturity, ...), (ii) to differentiate between methods that detect the noise ratio and those that detect noisy samples. (the latter are more difficult).
Vincent Lemaire Research Scientist
http://vincentlemaire-labs.fr
Under the supervision of the CEO, the project manager specialized in voice technologies, will be in charge of the projects of production of language resources and the coordination of RnD projects.
His responsibilities include the design/specification of language resources , the implementation of production media and platforms, the performance of quality controls and the validation, recruitment and management of staff involved in projects.
He/she will be in charge of reviewing the current workflows of producing language resources.
This position is an excellent opportunity for qualified, creative and motivated candidates, who wish to actively participate in the development of the field of language engineering.
The position is based in Paris 13th.
Salary : depending on qualifications and experience (between 40-50K€ gross).
Other advantages: supplementary insurance and restaurant
vouchers*Profile sought*
- BAC+5 in computer science specialized in voice technologies. Proven experience in the field of research (scientific publications ) will be a plus
- Experience and/or good knowledge in speech data collection with expertise in phonetics and transcription and annotation tools
- Experience in speech technologies (speech recognition/synthesis , modeling) and commonly used tools to produce, collect and validate data quality
- Ability to experiment with different techniques for the improvement or production of tools - Experience in international project management- Good command of Python- Good knowledge of scripting languages: bash, Python , Perl
- Good knowledge of Linux and free software - Ability to work both independently and as part of a team, in particular ability to supervise members of a multidisciplinary team- Dynamic and communicative, flexible and ready to work on a variety of tasks,
- Fluent English with ability to write user guides, administrative documents and reports, and Good command of French.
Knowledge of other languages will be a plus
- Citizen of a country of the European Union or in possession of a residence permit authorizing to work in France
*The Company*
ELDA is a human-sized SAS (15 people) and the operational body of the ELRA association (European Association for Language Resources).
ELRA was set up in February 1995, with the support of the European Commission, to promote the development and exploitation of linguistic resources (LAN) in all usable electronic forms, in particular in the form of oral and written corpora, lexicons and termbases.
The role of this non-profit association is to promote the production of RL, to collect and validate them, and finally to make them available to users.
The association also collects information on market needs and trends.
For more information about ELDA/ELRA, see:http://www.elda.org
Applications should be sent by email (cover letter and curriculum vitae) to:ELDA 9 rue des Cordelières 75013 Paris FRANCE
Email: [email protected]
*Deadline for application: November 28 2022, 13:00 CEST* One three-year PhD grant on Extracting information from clinical documents in a multilingual perspective is offered by the Doctoral Program in Brain, Mind & Computer Science (BMCS, http://hit.psy.unipd.it/BMCS) at the University of Padua, jointly with the Natural Language Processing research unit (https://ict.fbk.eu/units/nlp/) at Fondazione Bruno Kessler (Trento, Italy), where most of the research activities will be conducted.
The language of the PhD programme is English.
The deadline for application is: November 28 2022, 13:00 CEST
For more information, the call, and applications look at: http://hit.psy.unipd.it/BMCS/admission
The candidate will have the unique opportunity to explore different fields (Natural Language Processing, Machine Learning, Health & Well-Being) being coached by very experienced teammates. The involved PhD will work an international environment at Fondazione Bruno Kessler (Trento, Italy).
Fondazione Bruno Kessler is an internationally well-known research center, whose information technology department ranks first among the Engineering and Information Science research centers in Italy.
The Natural Language Processing research unit (https://ict.fbk.eu/units/nlp/) is an internationally well known research group focused on text mining (information extraction and ontology population from text, analysis of the sentiment and of the emotional content of texts); conversational agents (task oriented dialogue systems, question answering, generation of persuasive messages); and development of linguistic resources, particularly for the Italian language.
To get in contact with the NLP research unit and discuss about the opportunities of this call, contact Alberto Lavelli ([email protected])
The Doctoral Program in Brain, Mind & Computer Science (BMCS) emerges from the close collaboration between faculty from psychology, cognitive neuroscience and information science around the unifying topic of human-computer interaction. Its program rests on the assumption that the ability to work in groups with people of different background is now a fundamental condition to produce scientific excellence and to develop innovative skills that can be spent on the job market.
****Required/Preferred Candidate Skills and Competencies**** The candidate should possess basic knowledge on Natural Language Processing and Machine Learning techniques (particularly deep learning architectures).
Experience on biomedical/clinical data will be a plus. Basic programming skills (e.g. Python) would complete the profile.
Proficiency in English is required, basic knowledge of Italian preferable.
****Instructions for applicants**** Interested applicants are invited to apply following the instructions given inhttps://pica.cineca.it/unipd/dottorati38pnrr by November 28 2022, 13:00 CEST
For further information, please contact: Alberto Lavelli ([email protected])
OPEN POSITIONS in Paris (France)
The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context, acting as the distribution agency of the European Language Resources Association (ELRA), is currently seeking to fill both positions:
- Programme Manager (m/f)- Programme Manager in Speech Technologies (m/f)
Both positions are permanent and for immediate vacancy.
All details are available @https://bit.ly/3G3PG1Z https://t.co/7bwSMWookR
ELDA (Agency for the Evaluation and Distribution of Language Resources ), a company specializing in language technologies in an international context, is looking to immediately fill a position of *Program Manager for its international projects*.
*Job description*
Under the supervision of the CEO, the Program Director will be in charge of leading collaborative projects within international (particularly European) consortia in the field of language technologies and artificial intelligence.
He/she will be responsible for coordinating these projects internally and with other partners, including developing schedules and monitoring them, managing the project team, implementing deliveries, writing reports, attending project meetings.
He/she will work collaboratively with external partners in all project phases , including building and managing project teams. He/she may be required to participate in the submission of calls in relation to external partners
This position is an excellent opportunity for creative and motivated candidates , who wish to actively contribute to the development of the field of language technologies.
The position is based in Paris 13th.Salary : depending on qualifications and experience (between 40-60K€ gross).
Other advantages: supplementary insurance and restaurant
vouchers*Profile sought*
- PhD in natural language processing or equivalent - Knowledge and experience of different natural language processing techniques (speech processing, data mining, machine translation, etc.) - Experience in managing projects under European funding- Experience in team management- Good knowledge and practical experience of project management tools- Very good level of English, with very strong skills in technical and editorial English - Dynamic and communicative, flexible and ready to work on various tasks in parallel - Ability to work both independently and in a multidisciplinary team- Citizenship of a country of the European Union or possession of a residence permit authorising to work in France
*The company*
ELDA es t a human-sized SAS (15 people) and the operational body of the ELRA association (European Association for Language Resources).
ELRA was set up in February 1995, with the support of the European Commission, to promote the development and exploitation of linguistic resources (LAN) in all usable electronic forms, in particular in the form of oral and written corpora, lexicons and termbases.
The role of this non-profit association is to promote the production of RL, to collect and validate them, and finally to make them available to users. The association also collects information on market needs and trends.
For more information about ELDA/ELRA, see:http://www.elda.org
Applications should be sent by email (cover letter and curriculum vitae) to:ELDA 9 rue des Cordelières 75013 Paris FRANCE Email: