Thesis offer to Orange Labs and IRISA laboratories (in Lannion): Deep learning for estimating the impact of drones on a mobile network
The objective of this thesis is to model and evaluate the impact of connected drone trajectories on a radio access network. This must be done taking into account the roads and cellular coverage of one or more operators. The purpose of the model will be to take into account planned drone routes to (1) minimize the traffic impact on a given RAN (radio access network), (2) avoid interference between drones and sensitive areas, and (3) maintain good QoE for user (EU) equipment.
Required skills: knowledge of mobile networks, Deep Learning (ideally PyTorch or Tensorflow), graph theory, Python, Shell, Matlab would be a plus, good communication in English orally and in writing, teamwork, curiosity and open-mindedness.
How to apply? https://orange.jobs/jobs/ offer.do?joid=112418&lang=EN
As part of a scientific collaboration with Anabasis Assets (https://www.anabasis-assets. com/fr/), the CIAD laboratory in Dijon is looking for a candidate for a thesis in the field of artificial intelligence and more particularly in the field of ontology learning. This field aims to improve the use of ontologies using machine learning techniques to automate (in part) the creation and maintenance of ontologies. In this case, the constituent elements of ontologies (Terminological Box, Assertional Box, rules of reasoning) can be "learned" from different resources.
The field of ontology learning represents a set of methods and techniques for the formal development of ontologies using machine learning algorithms. This concept has already been studied in many recent journals, and makes it possible to provide assistance in the design of ontologies to reduce the cost and production time. The objective of this thesis is to study the field of "ontology learning" and to propose formalization and implementation tracks on use cases provided by the company in or several of these subdomains: automatic taxonomy construction, learning of non-hierarchical relations, rule discovery, learning ontology population, learning ontology enrichment, leaning-based reasoning.
The candidate must have a good knowledge of semantic web, machine learning and programming. The candidate must have good writing and oral skills in both French and English. The work will take place partly in the laboratory within a team of 15 people and within the company. A good sense of interpersonal skills and teamwork will be appreciated. Interested persons are requested to send their application files (CV, cover letter, transcripts, letter of recommendation) to Christophe Nicolle ([email protected]) before 31 May. A detailed topic and a timetable for completion will be sent to applicants upon request
We are recruiting a post-doc at CIAD (Deep Learning, Explainable AI), as part of the ANR DALHAI: Hybrid artificial intelligence design of plasmonic ALU.
Recruitment is planned as soon as possible for a minimum period of 12 months (with the possibility of extension according to profile).
The place of practice is the CIAD laboratory in Dijon. The project team is currently made up of 11 people (3 EC in physics, 3 EC in AI, 2 Postdocs in Physics, 1 Doctor in physics, 2 engineers in computer science). To complete this group we are looking for a PhD in Computer Science with a background in AI. The plus would be a good appetite for research, group work and motivating projects. The project started a year ago with excellent results that we want to consolidate in scientific publications and continue research on the hybridization part of deep learning with logical rules to meet the challenges of explainability.
Feel free to send a CV and all the elements
Thank you in advance for your help in circulating this offer in your networks. Contact: Christophe Nicolle ([email protected])
The LaBRI research lab at the University of Bordeaux is currently seeking highly motivated candidates with experience and interest in knowledge representation and reasoning, databases, and/or logic in CS to take part in the INTENDED AI Chair (intended.labri.fr), whose aim is to develop principled and effective methods for handling imperfect data.
The three-year PhD position will start on October 1, 2022. Details on the topic, research environment, and application procedure can be found on the project website:
https://intended.labri.fr/hiring.html
The deadline for submitting applications is *June 1, 2022*.
If you're interested in applying, or want more information, please do get in touch!
Post-doctoral position Representing and enriching events in knowledge graphs Key words: machine learning on graphs, knowledge graph, event prediction
Context: XP-event project (2021-2024) An event is defined as “anything that happens, anything that fits over time”: meetings, phone calls, purchases, but also business buyouts, change of management, health crises, etc. The events are shared at through various communication channels that can be private (internal documentation, emails, Slack, Teams, phone, etc.) or public (press, Twitter, Facebook, etc.). Knowledge of these events is essential for humans to make decisions which themselves will have an impact on future events. Many innovative applications can benefit or even emerge from a technology capable of extracting events from various sources, representing them, aggregating them and exploiting them to predict future events. We can for example cite: anticipating demand for sanitary products, the supervision of cultural, advertising or festive events, but also the study of competition, the study of commercial markets, etc. .
One of the main obstacles to the deployment of these applications is the excessively high cost of their development when it is carried out on an ad hoc basis by competing players. The XP-Event project proposes to respond to this difficulty by setting up a common base for all the applications organized around the notion of event. This project is led by a consortium naturally formed by two companies (GeoTrend and Emvista) and a research team from the IRIT laboratory sharing this vision and each having significant scientific and technological heritage in the field.
Position Description The candidate will contribute to the tasks in which IRIT is involved, and will be more particularly in charge of realizing and implementing the proposed solutions. The first task concerns the representation of event graphs. The first task will be to define an adapted ontology and a process allowing to exploit it to access or represent in RDF the graphs of the industrial partners of the project, which are graphs of quite different nature. The second task aims at defining a process to evaluate the quality of the event graphs. Evaluation will be based on the ontology structure as well as on reasoning from the knowledge graph. The third task concerns the enrichment of these graphs. Two types of approaches will be implemented in the project, and for each of them research will be needed to advance the state of the art. The first approach consists in extracting information from texts. Each of the industrial partners already has its own processing chain that it will improve and unify. The second approach consists in exploiting the current state of a graph but also the structure of an event in the ontology to suggest the addition of new nodes or new relations to the graph. This approach will be implemented through learning algorithms from graph.
Requirements for this position Applicants are required to have a PhD in computer science, and strong background ideally in two areas of artificial intelligence: semantic web technologies (ontology engineering, linked data management and querying, SPARQL, SHACL, RuleML, ...), and machine learning from graphs and vector representations, recursive neural networks, etc. Good programming skills (Python, OWL API) and experience in participating in collaborative projects is required. In addition, the candidate must have a taste for innovation, and the ability to dialogue and collaborate with industrial partners. Experience in managing graph warehouses (Virtuoso, Strabon, Neo4j...) is desired. Fluency in written / spoken English is required too. Fluency in French language will be a plus.
Work environment Location: Institut de Recherche en informatique de Toulouse (IRIT) - UPS, 118 Route de Narbonne F-31062 Toulouse Cedex - France and UT2J, 5 allées Antonio Machado F-31300 Toulouse
Duration: 24 months – starting as soon as possible (at best 2 months after application)
Host team: MELODI https://www.irit.fr/en/departement/dep-artificial-intelligence/melodi-team/
The candidate will work with four academic researchers from MELODI (F. Benamara, Ph. Muller, N. Aussenac-Gilles and N. Hernandez). He will collaborate with the partner companies in the project, namely Geotrend, located in Toulouse, and Emvista, located in Montpellier.
Income: between 2300 and 2800 euros before taxes (brut) monthly according to past experience
How to apply? Applicants should send their application files to the contact persons listed here below. Application files should contain at least a full Curriculum Vitae including a complete list of publications, a cover letter indicating their research interests, achievements to date and vision for the future, as well as either support letters or the name of 2 persons that have worked with them.
Applicants should contact: N. Aussenac-Gilles
Two open faculty positions at University of Patras, Electrical and Computer Engineering Department:
1. Electronic Systems, Assistant Professor (tenure track) or Associate Professor 2. Wireless Communications, Assistant Professor (tenure track)
Potential applicants can use the link below using the reference codes APP26934 and APP26936 for position 1 and APP 26937 for position 2 (use reference codes in search box).
https://apella.minedu.gov.gr Application deadline: June 14th
Below follows the call in greek. Please forward the message to any potentially interested colleague!
You will find attached a 24-month post-doc/CDD offer at the ERPI laboratory (University of Lorraine) in Machine Learning with learning methods on the analysis of the impact of digital environments for education.
Digital environments for learning are increasingly numerous in high schools and universities. The PLEIADES project (Lorraine Digital Environment Project for DurablES Learning) aims to deploy new digital environments and analyze their impacts. The use of data from these environments during educational sessions will improve our understanding of these new uses of digital technology for education, analyze and improve learning processes. On the scientific level, we will be interested in learning the data from the pedagogical sessions. We want to model the measurement and prediction of gain during pedagogical sessions with high school students and students from the University of Lorraine using machine learning models using data from digital environments. It will be a question of measuring and evaluating the impact of these digital environments for learning and of formulating recommendations to adapt their uses according to pedagogical typologies.
The candidate will join the research team of the PLEIADES project. PLEIADES (Lorraine Digital Environment Project for DurablES Learning of the University of Lorraine) aims to create an inclusive digital environment at the service of the university community. By mobilizing constant and quality support, the University of Lorraine wants to place digital technology through this project, at the service of weaving links between the university community and at the service of an embodied belonging and experience. Digital technology must be a catalyst for interactions and cooperation between all actors, to break distances (social, disciplinary, geographical) and to support the fundamentally collective dimension of learning, both that of knowledge and, beyond that, that of autonomy and discernment. This project is based on five programs:
PLEIADES-Territoires: create links between the university and the high schools of the Vosges territory by relying on an immersive environment in collaboration with the services of the rectorate; PLEIADES-Trajectories: ensure the monitoring, progression and evaluation of skills through the use of an e-portfolio;
PLEIADES-Environment: enrich learning through the use of virtual reality technologies and advanced features on the course platform; PLEIADES-Relations: boost language learning by connecting university students with students from foreign universities. PLEAIDES-Espaces: create virtualization spaces to meet the needs of students.
The candidate will work on the "PLEIADES-Territories" axis. It will ensure the collection of data, the learning of data and the analysis of data and uses and the restitution of results. The candidate will work in collaboration with two other post-doctoral fellows working on 2 other axes of the PLEIADES project.
Desired skills: - Thesis in Computer Science. - Data collection or training or recommendation systems or data mining. - Knowledge of Living Lab approaches or collaborative workshop facilitation would be a plus.
Contract Start Date: September 2022.
Salary: from 2130€ gross to 2948€ gross (to be negotiated according to the experience of the researcher)
Duration of contract: 24 months.
To apply: Send CV and cover letter to davy.monticolo@univ-Lorraine. fr
Deadline: June 10, 2022
I would like to share two thesis offers and an engineer position in the domains of virtual reality, computer vision, and AI.
- Thesis "Modeling 6DoF Navigation and the Impact of Low Vision in Immersive VR Contexts" in the context of ANR CREATTIVE3D (https://project.inria.fr/creattive3d/)
Offer and application link: https://recrutement.inria.fr/public/classic/en/offres/2022-04807
- Thesis "Data modeling and graph neural networks for multimodal film analysis" in the context of ANR TRACTIVE (https://www.i3s.unice.fr/TRACTIVE/) Offer and instructions to apply: https://www.i3s.unice.fr/~sassatelli/files/PhDposition_DS-ML-films_IRIT.pdf
- Engineer in computer vision and data processing in the context of ANR TRACTIVE (https://www.i3s.unice.fr/TRACTIVE/) Offer and application link: https://recrutement.inria.fr/public/classic/en/offres/2022-04592
Hate Speech (HS) and harassment are particularly widespread in online communication, especially due to users' freedom and anonymity and the lack of regulation provided by social media platforms. This phenomenon has determined a growing interest in using artificial intelligence and Natural Language Processing techniques to address social and ethical issues. An extensive body of work has been proposed to automatically detect HS relying on a variety of deep learning methods (Founta and Nunes, 2018; Schmidt and Wiegand, 2017). Most research focus on HS as expressed in texts without taking into account the contexts in which they have been uttered. This PhD aims to bridge the gap by investigating for the first time how HS are expressed and detected in multi-party dialogues. We will propose new dialogue datasets for HS detection as well as new context-based deep learning methods that leverage the conversation thread to account for hateful contents and how they evolve as the dialogue proceeds.
This project is part of the DesCartes program, https://www.cnrsatcreate.cnrs.fr/descartes/, that aims to develop disruptive hybrid AI to serve the smart city and to enable optimized decision-making in complex situations, encountered for critical urban systems.
References Paula Fortuna, Sérgio Nunes:A Survey on Automatic Detection of Hate Speech in Text. ACM Comput. Surv. 51(4): 85:1-85:30 (2018)
Anna Schmidt, Michael Wiegand: A Survey on Hate Speech Detection using Natural Language Processing. SocialNLP@EACL 2017: 1-10
Ideal Candidate** --> Master degree in Computer science (compulsory) with solid background in NLP and/or machine learning --> A good experience in deep learning approaches for NLP --> Good programming skills in Python
Supervision** Jian Su (Institute for Infocomm Research A*STAR, https://colips.org/~sujian/), Shafiq Joty (Nanyang Technological University (NTU), https://raihanjoty.github.io/) and Farah Benamara (Toulouse University, https://www.irit.fr/~Farah.Benamara/)
4 years
CNRS@CREATE Singapore (https://www.cnrsatcreate.cnrs.fr/about-us/)
Please find the allowances under COVERAGE at https://www.a-star.edu.sg/Scholarships/for-graduate-studies/a-star-cis-scholarship
SINGA students may be offered the A*STAR Merit Award should they have stellar credentials and are recommended for the higher award by the final interview panel. Students under the AMA (A*STAR Merit Award) receive a monthly stipend of S$3,200.
**Deadline to apply** May 23th 2022