Postdoc position at ANPL, Technion The Autonomous Navigation and Perception Lab (ANPL) at the Technion - Israel Institute of Technology, has an opening for a postdoctoral research fellow.
ANPL's research focuses on robust and computationally efficient autonomous perception, learning and planning under uncertainty. Research in the lab is highly multi-disciplinary, involving fundamental and algorithmic foundations, as well as applied research. We are looking for a highly motivated strong candidate that will be making key contributions to ongoing research projects (see ANPL website) while also developing his/her own research line.
Applicants should have a Ph.D. (or about to graduate) and a *strong* background and interest in areas such as
- single and multi-robot planning under uncertainty
- probabilistic inference
- visual and semantic SLAM/perception
- machine (deep) learning
Applicants should submit a cover letter that briefly describes their background and career plans, CV and three professional references. Please send all application materials to [email protected]. Starting date is flexible, applications will be reviewed on a rolling basis.
For more information, please visit http://vindelman.net.technion.ac.il or contact via email.
Vadim Indelman, Ph.D. Associate Professor Autonomous Navigation and Perception Lab Technion - Israel Institute of Technology
Tel: +972-4-829-3815 Email: [email protected] Web: http://vindelman.net.technion.ac.il/
Ph.D. proposal for the project ANR JCJC CREMA advised by Marco Dinarelli, Didier Schwab and Emmanuelle Esperança-Rodier
Coreference REsolution into MAchine Translation (CREMA)
The CREMA project aims at improving Document-Level Neural Machine Translation models (DL-NMT) by integrating a module for coreference resolution in a neural translation model. DL-NMT is currently one of the most interesting research directions, and it has remarkable fallout both on scientific and applicative domains.
Current models for DL-NMT integrate a context for improving translation consisting of a fixed number of previous sentences. Previous work has shown however that most of the words in a sentence can be correctly translated without any context. While words needing a context are relatively rare in sentences, their correct translation has a remarkable impact on the quality of document translations. Moreover, information contained in the context and needed for the correct translation of some words are rare as well.
As consequence, the use of a fixed number of sentences as context may drawn in the noise words bringing such information. Tea literature has shown that the most ambiguous words to be translated are those involved in discourse phenomena, in particular anaphora and coreferences.
The objective of the project CREMA is thus to integrate a coreference resolution module in a neural translation system. This module will detect sentences containing words needing a context for their translation, as well as those sentences containing disambiguating clues. The translation model will be able thus to use only such sentences as context, instead of a fixed number of sentences chosen in advance.
The Ph.D. program will focus at first on creating an end-to-end coreference resolution system. Such system will be inspired from existing systems and will be evaluated on the same data. In a second time, the Ph.D. objective will be to integrate the coreference resolution model into an existing neural translation system. Tea complete system will be evaluated on the traditional data sets used for the evaluation of DL-NMT models.
Candidate profile: - Strong experience in programming and machine learning for Natural Language Processing (NLP), particularly in deep learning
- Master with a component on NLP or computational linguistics
- Good knowledge of the French and / or English language
Practical details: - Ph.D. begin is between January and March 2022
- Full time doctoral contract at the LIG laboratory (Getalp group) for 3 years (raw salary: 1768 € per month)
Scientific environment: The Ph.D. program will take place in the Getalp group of the LIG laboratory ( https://lig-getalp.imag.fr/ ).
The Ph.D. candidate will be integrated in the group offering a cosmopolitan, motivating and pleasant environment.
The candidate will be allowed to participate in national (French) and international events such as conferences or summer schools, and he / she will be provided with adequate hardware material (personal computer, access to the computing servers with GPU of the LIG, and to the CNRS super-computer Jean-Zay).
How to apply? Candidates must hold a master degree in computer science or NLP (at the beginning of the Ph.D. program). They must have a good knowledge in machine learning and possibly experience in corpus collection and data management.
They must have as well a good knowledge of the French and / or English language.
Some experience in the domain of coreference resolution and / or neural machine translation is a plus.
Applications must be sent no later than October 31 2021 and must contain:
CV, motivation letter, master marks notebook, recommendation letter; they must be sent to
Marco Dinarelli ( [email protected] ), Didier Schwab ( [email protected] ) and Emmanuelle Esperança-Rodier ( [email protected] ).
Hello, Two full-time AER posts from 12/01/21 to 08/31/22 (9 months) are open at the University of Orleans, with teaching at the UFR ST (License / Master in computer science and Miage ) and research at the LIFO laboratory.
A post on 01/01/22 may be considered. The application deadline is 04/11/2021 at 4 p.m.
The job description is available here: https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ATERListesOffresPubliees/0450855K/FOPC_42316.pdf
Interested persons are invited to consult this page: https://www.univ-orleans.fr/fr/univ/universite/travailler-luniversite/personnels-enseignants-et-chercheurs/attaches-temporaires
Contact : research: [email protected] teaching: [email protected]
Do not hesitate to apply! Regards, Bich dao
CDD researcher position (24 months)
Representation and enrichment of event graphs
Keywords: Learning from graphs, Knowledge graphs, 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 private (internal documentation, emails, Slack, Teams, phone, etc.) or public (press, Twitter, Facebook, etc.) communication channels. Knowledge of these events is essential for humans to make decisions that 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, supervising 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 ad hoc 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 supported 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.
Job Description The candidate will contribute to the tasks in which IRIT is involved, and will be more particularly responsible for realizing and implementing the proposed solutions. The first task concerns the representation of event graphs . It will first be a question of defining an adapted ontology then 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 is to define a process for evaluating the quality of the event graphs. We will rely on the structure of the ontology 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 necessary to advance the state of the art. The first approach is to extract 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 in the graph. This approach will be implemented using learning algorithms from the graph.
Requirements for this position Candidates must have a doctorate in computer science, have a solid experience, ideally in two fields of artificial intelligence: semantic web technologies, (ontology engineering, management and querying of linked data, SPARQL, SHACL, RuleML, ...), and machine learning from graphs and vector representations, recursive neural networks, etc. A good mastery in programming (Python, OWL API) and experience in participating in collaborative projects is required. In addition, the candidate must have a taste for innovation, capacity for dialogue and collaboration with industrial partners. Experience in managing graph warehouses (Virtuoso, Strabo, Neo4j ...) is desirable.
Working environment Location: Toulouse Computer Science Research Institute (IRIT) - UPS, 118 Route de Narbonne F-31062 Toulouse Cedex and UT2J, 5 allées Antonio Machado 31300 Toulouse
Duration: 24 months - starting January 1, 2022
Reception team: MELODI https://www.irit.fr/en/departement/dep-artificial-intelligence/melodi-team/
The candidate will work with the permanent staff of MELODI involved in the project (F. Benamara, Ph. Muller, N. Aussenac-Gilles and N. Hernandez). It will work with the partner companies Geotrend, located in Toulouse, and Emvista, located in Montpellier.
Salary : between 2300 and 2780 euros gross monthly according to experience
How to apply Candidates must send their file before November 15, 2021 by email to the addresses below. The file must include at least the following documents: complete CV with a list of publications, cover letter specifying the interests, results and perspectives of your research, as well as letters of support or the names of three references among your collaborators. Contact for any additional information: N. Aussenac-Gilles [email protected] , N. Hernandez [email protected] ,
Nathalie Aussenac-Gilles IRIT - MELODI team Paul Sabatier University 118, route de Narbonne 31062 TOULOUSE Cedex 9
http://www.irit.fr/~Nathalie.Aussenac-Gilles [email protected] Tel: +33 5 61 55 82 93 Fax: +33 5 61 55 62 58
Master Internship Position: Deep Learning architectures for generating skeleton-based human motion
Interested in Deep Learning and human motion analysis ? Apply to our Master internship position in deep generative models for skeleton-based human motion.
As part of the ANR DELEGATION project, you will have the opportunity to continue with a funded PhD in our team !
More information below or at : https://maxime-devanne.com/delegation/jobs/internship_position_delegation_2022.pdf
Context: Human motion analysis is crucial for studying people and understanding howthey behave, communicate and interact with real world environments. Dueto the complex nature of body movements as well as the high cost of motioncapture systems, acquisition of human motion is not straightforward and thusconstraints data production. Hopefully, recent approaches estimating humanposes from videos offer new opportunities to analyze skeleton-based human mo-tion. While skeleton-based human motion analysis has been extensivelystudied for behavior understanding like action recognition, some efforts are yetto be done for the task of human motion generation. Particularly, the automaticgeneration of motion sequences is beneficial for rapidly increasing the amountof data and improving Deep Learning-based analysis algorithms.
Since several years, new image generation paradigms have been possiblethanks to the appearance of Generative Adversarial Networks (GAN) which have proved to be extremely efficient for many image generation tasks and hu-man posture estimation. Although these networks are very efficient, theirexplainability and control still remain challenging tasks. Differently, other gen-erative models have also emerged by considering the data distribution duringtraining like Variational AutoEncoder (VAE) and Flow-based networks.However, when it comes to human motion, many challenges remain to be solved,in particular when passing from the static case to the dynamic case. Firstwork addressing deep generative models for human motion have considered mo-tion capture (mocap) data allowing to accurately extract body parts positionsalong the time. Hence, aforementioned generative architectures have been suc-cessively employed for generating mocap-based human motion sequences. Differently, we consider noisy skeleton data estimated from videos as it iseasily applicable in real-world scenarios for the general public.
Goal of the project: The goal of this internship is to provide guidelines in building deep genera-tive models for skeleton-based human motion sequences. Inspiring from recenteffective Deep Learning-based approaches, the aim is to gener-ate full skeleton-based motion sequences without access to successive poses asprior information as it can be done in prediction tasks. It is therefore crucialto investigate how deep generative models can handle such noisy and possiblyincomplete data in order to generate novel motion sequences as natural andvariable as possible
In particular, the candidate will work on the following tasks: - Deep Learning architectures for skeleton-based human motion: investigation and assessment of the influence of different deep network ar- chitectures for capturing complex human motion features. Particularly, the goal of this task is to theoretically and empirically analyze the per- formance of existing architectures like CNN, RNN and GCN for modeling skeleton-based human motion.
- Deep generative models adapted to skeleton data: based on stud- ies from the previous task, the goal is to build generative models upon the previously identified meaningful spaces where skeleton sequences are represented. Therefore, the candidate will investigate different generative models, like GAN, VAE and Flow-based models, in order to propose and develop a complete Deep Learning model for generating skeleton-based human motions.
- Evaluation of deep generative models: in order to validate the pro- posed model, experimental evaluation is crucial. In comparison to motion recognition where classification accuracy is a natural way to assess an ap- proach, evaluating the task of motion generation is not as straightforward. Dedicated metrics evaluating both naturalness and diversity of generated sequences as well as the impact of new generated sequences in a classifi- cation task will be considered.
Prerequisites: The candidate must fit the following requirements: - Registered in Master 2 or last year of Engineering School (or equivalent) in Computer Science
- Advanced skills in Python programming are mandatory
- Good skills in Machine Learning & Deep Learning using related libraries (scikit-learn, Tensorflow, Pytorch, etc.) are required
- Knowledge and/or a first experience in human motion analysis will be appreciated
Research environment: The proposed internship will be carried out within the MSD (Modeling and Data Science) team from the IRIMAS Institute. It will be part of the ANR DELEGATION project starting in 2022 for 4 years. Hence, there is a great opportunity to continue with a PhD in our team on the same topic/project.
Application: For further information or for applying, candidates should send a CV, academic records, personal projects (e.g. github repo) and a motivation letter to [email protected].
--- Maxime Devanne Maitre de Conférences en Informatique ENSISA/IRIMAS Université de Haute-Alsace https://maxime-devanne.com
From: Frederique Bordignon [email protected]
Date: Mon, 11 Oct 2021 10:18:32 +0200
https://nanobubbles.hypotheses.org/280
Hello,
As part of the ERC Synergy NanoBubbles project, we are looking for a "Digital Humanities" profile to help us:
- to build corpora of scientific texts associated with their metadata, for example using Dimensions or Scopus and archives such as ISTEX.
- then to treat them so that they nourish the work and the reflection of sociologists, historians, linguists and computer scientists of the team.
Check out the offer here https://nanobubbles.hypotheses.org/280 and do not hesitate to apply to join a European team and multidisciplinary!
Regards, Frederique Bordignon
Three Deep learning and Computer Vision Postdoc Positions Jointly Supervised in Aristotle University of Thessaloniki, Greece and Henan University, China
Position Description Three postdoctoral positions are immediately available at the Data Science and Artificial Intelligence Team/Lab, Henan University; the postdoc fellows will be co-supervised by Prof. Ioannis Pitas from Aristotle University of Thessaloniki, Greece.
The postdoc fellows can stay 4-5 months with Prof. Ioannis Pitas at Aristotle University of Thessaloniki, Greece; but they should stay in China for at least 7-8 months per year.
Position Location The postdocs will be paid by Henan University, China. They will stay in Aristotle University of Thessaloniki (4-5 months), Thessaloniki, Greece, and Henan University (7-8 months), Kaifeng, China
Subject Areas Deep learning and Computer Vision, including but not limited to Object Detection/Recognition, Region Segmentation and related topics.
Salary 1800~2200 EUR net per month.
Application Deadline December 31st, 2022
Duration of the contract The duration of the contract is 24~36 months, and can be extended up to 48 months.
Salary a) The monthly net income of the Postdoctoral researcher is around 1800~2200 EUR (after taxes being deducted), paid by Henan University.
b) Additionally, the University will provide a total amount of 100,000 RMB as the candidate's research funding, which can be used for research purposes such as conferences, traveling, meetings, etc. Note: This research funding is once for all, not per year.
Note: The Living expenses in Kaifeng is relatively low. For instance,
Accommodation cost (two bedrooms, one living room) in Kaifeng is about 150~200 EUR per month. Food cost in KaiFeng is about 100-150 EUR per month.
Requirements/Qualifications 1. The successful candidates should hold a PhD degree in machine learning, or computer vision/pattern recognition, or other related fields.
2. At the time of appointment, the applicants must be no more than 35 years old in age.
3. The applicants should have good publication records in the above-mentioned fields.
About Aristotle University of Thessaloniki, Greece
Artificial Intelligence and Information Analysis Lab (https://aiia.csd.auth.gr/) Aristotle at University of Thessaloniki (AUTH) has one the best R&D records in Europe (72+R&D projects, mostly EC funded).
The Department of Informatics at AUTH is ranked 1st among the Greek Universities and 106th internationally in the field of Mathematics & Computer Science for 2019 in the Leiden Ranking list, which mainly depends on the scientific impact and staff publications.
It is also ranked 1st among the Greek Universities in the international ranking list Guide2Research, for 2021, having 5 faculty members in the list of top scientists in the scientific area of Computer Science and Electronics.
Aristotle University of Thessaloniki (AUTH), established in 1925, is among the most prestigious universities in Greece. Its computer science discipline is ranked #335 in the world in the US News University Ranking 2021.
Prof. Ioannis Pitas (34100+ citations, h-index 87) is an IEEE Fellow, EURASIP fellow and a world-renowned professor in multimedia systems and computer vision.
Homepage: https://aiia.csd.auth.gr/computer-vision-machine-learning/#people
https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=en
About Henan University and KaiFeng, China
Henan University has a history of 109 years, and has been selected as the ''Double First Class'' University by the Ministry of Education of China, see http://en.henu.edu.cn/.
KaiFeng is a historical city (capital of Song Dynasty) and a famous tourist destination, see
https://en.wikipedia.org/wiki/Kaifeng.
How to apply Applications are only accepted via Email.
All the following documents should be sent to Prof. Ioannis Pitas and Prof. Chongsheng Zhang simultaneously, at [email protected] and [email protected], with the email subject ''Postdoc application'':
- 1. A cover letter
- 2. Full CV and list of publications
- 3. Two reference letters.
If you have any questions, do not hesitate to contact Prof. Ioannis Pitas and Prof. Chongsheng Zhang via Email.
Hello everyone,
We are proposing a thesis funded by the SystemX Technological Research Institute and supervised by the IBISC (Computing, Bio-Computing and Complex Systems) laboratory at the University of Paris-Saclay, Evry site.
The title of the thesis is " Risk assessment and resilience of an autonomous vehicle "
The theme concerns the search for threats and feared events (cyberattacks, software failures, breakdowns) at the functional level as part of the analysis of risks related to the autonomous driving of vehicles and the response to be provided to increase resilience.
The objective of the thesis is the development of a method to validate that the functional anomalies are correctly taken into account by the decision module to maintain the highest possible operational safety.
The method, starting from agent-based modeling and simulation of autonomous vehicle behavior, will provide a set of feared events with their targets and propagation paths, and their potential impact on operational safety.
A more detailed study, using formal verification tools such as model-checking, will confirm the dangerousness of these events and assess their criticality.
A more detailed description is attached.
Contacts: - Guillaume HUTZLER (IBISC) [email protected] , Jeremy SOBIERAJ (IRT SystemX) Jeremy SOBIERAJ [email protected]
Regards, Guillaume Hutzler
Postdoc position in Artificial Intelligence
Security hardening based on multi-agent system verification in the context of the connected car infrastructure
Keywords: Security, Formal Verification, Model Checking of Multi-Agent Systems, Security Risk Management, Attack Modelling
Description Recently, classic verification approaches such as model checking have been extended to handle multi-agent systems. These are systems that encapsulate the behavior of two or more rational agents interacting among them in a cooperative or adversarial way, aiming at a designed goal.
In system security checking, a malicious attack can be seen as an attempt of an intruder to gain unauthorized access to a resource or as an attempt to compromise the system integrity.
The envisioned approach is: given an attack model of the system (such an attack graph), to model the interactions between the attacker & defender as a game on this attack model and use multi-agent system verification techniques to determine optimal defense strategies.
Goals The global objective of this postdoc is the use of methods and tools for security hardening in the context of the connected car infrastructure based on a formal verification approach.
Some of the challenges of this postdoc will be to answer to the following questions:
How can we select security counter-measures ensuring the best trade-off between security level achieved and other constraints?
How can we capture the dynamic between the attacker and the defender?
How can we integrate these decision support tools in the classical risk management process such as defined by the ISO/IEC 27005 without redefining it completely?
Profile and skills required
- PhD in computer science, mathematics, or related fields.
- Strong computer science and/or mathematical background (with particular attention on formal methods, logic, and security). - Good programming skills.
- Good level in written and spoken English.
How to apply If you are interested you can apply by sending your CV and motivation letter to: [email protected] and [email protected]
Deadline for application submission: October 29, 2021.
The objective of the thesis is to design a collaborative system that will correct and complete the results of an optical recognition phase applied to partition images.
These results most often need to be edited manually in order to correct deficiencies.
He This will involve setting up a collaborative tool so that a community of Internet users (mobilized by our partner BnF) can make corrections or suggest improvements.
This tool will be associated with a data reconciliation method that will aggregate the contributions of each Internet user in order to converge on the best possible solution.
Techniques to study and implement cover several areas: collaborative editing, identification of differences between sources semi-structured, quality measurement, manual or automatic reconciliation. Combination of OMR and crowdsourcing is a promising way to resolve the issue efficient digitization of scores, in line with the principles of participatory sciences tives: free and open data, valuable contributions for users, openness to mediation and education.
We can rely on Conflict-free replicated data types (CRDT) which define the necessary and sufficient conditions on the types and operations to guarantee the convergence, without user intervention to achieve consensus.
Collaborative text editors (e.g. Google docs, Overleaf for LaTeX, or Atom) are examples of use cases that relate to the intended objectives.
It will always be necessary to take in include in the solutions studied the constraints specific to the coding of musical scores.
The main steps to consider are as follows:
• Review of the state of the art for collaborative publishing and crowdsourcing methods, with focus on structured documents on the one hand, music publishing on the other.
• Design of an annotation system to record valid editing operations on a digitized score.
• Design of automatic or semi-manual reconciliation algorithms, leading to a version optimizing the quality of collaborative correction.
• Participation in the design of an interface to suggest corrections on a score, monitoring of the crowdsourcing campaign (organized by the BnF), implementation and evaluation of previous methods and algorithms.
• Design of a collaborative editor in real time dedicated to scoring. musical. 3 Environment
The thesis will take place in Paris, at the Conservatoire National des Arts et Métiers (Cnam), in the team VERTIGO from the Cédric laboratory, under the supervision of Prof. Philippe Rigaux.
The team has developed and has maintained for several years a digital sheet music library, Neuma, which provides numerous corpora of noted music. It is involved in national projects (ANR Neuma, Munir, CollabScore) and international (H2020 Polifonia project)
The recruited person must have solid knowledge in databases, programming Python, distributed systems and ideally collaborative systems. A complementary skill in practice or theory of music is essential.
Contact: Philippe Rigaux, [email protected]
Hello everyone,
INSA Rouen Normandie is looking for a computer engineer for robotics (complete information attached).
Summary of missions : Technical support on the experimental platform of intelligent mobile robotics for research and teaching staff as well as students.
Profile sought : Engineer / Master in Computer Science.
Contract : 12 months renewable.
Remuneration : 2400-2800 € Gross depending on the profile of the person recruited.
Deadline for receipt of applications: Mid-October 2021.
Application :
Please send:
a complete and detailed curriculum vitae
a cover letter
the names and full contact details of two or three qualified individuals who can give an informed opinion on the application
By email to: [email protected] , [email protected] and [email protected]
Maxime Guériau Assistant Professor / Senior Lecturer LITIS - INSA Rouen Normandy
Hello everyone,
We have two postdoc offers in Montpellier between LIRMM (U. Montpellier / CNRS) and MISTEA (INRAE / SupAgro).
The first is a postdoc on data binding as part of the ANR DACE-DL:
https://docs.google.com/document/d/e/2PACX-1vTNvOng07Crjvwy7GbB8vKo6DhBYk0fMO3OAbQcg7pKJNxQBPE2dTKfMICX95Syyy9xLntWnIalVcJy/pub
Contact: Clement and Danai
The second is a postdoc / IR (with a stronger dimension on scientific project management) on ontology management and the LIRMM-Stanford-LifeWatch collaboration on the IRT NUMEV OntoPortal
https://docs.google.com/document/d/e/2PACX-1vRClUKi7gC416v1Oc7vAjg_4qm4JP2W49SjCi5zJjDKKIK6i_dhrqBwi3LhZ7oU9BhgsqL2Oi3tHBj1/pub
Contact: Clement
Recruitment planned between the end of 2021 and the very beginning of 2022. Thank you for circulating these two offers in your networks.
Regards Clement Jonquet
Dr. Clement JONQUET - PhD in Informatics Associate Research Scientist - INRAE (MISTEA) Associate Professor - University of Montpellier (LIRMM)
Coordinator of the SIFR, AgroPortal and D2KAB projects
http://www.lirmm.fr/~jonquet
CDD 12/24 MONTH - SESSION 2021
General information Corps: Post-Doc / Research Engineer
Family: Statistics
Employment-type: Statistic expert/Data processing
Nature: CDD
Degree: Bac+5/Bac+8
Contractual information
Contrat type: CDD
Contrat duration: 12/24 mois
Contrat start: period sep-dec 2021
Ratio: full time
Salary: with respect to degree/experience
Location: Reims
Contact Person in charge: Nicolas Passat, Guillaume Dollé, Stéphanie Salmon Email: [email protected], [email protected], [email protected]
Affectation Institut CReSTIC/LMR University: Université de Reims Champagne-Ardenne
Service: Address: UFR Sciences Exactes et Naturelles Campus Moulin de la Housse BP 1039 - 51687 Reims CEDEX 2
Missions The recruited person is in charge to: develop new algorithms and digital methods;
participate in the optimization and development of the project’s digital tools;
contribute to the porting of codes on the supercomputers made available;
participate in the dissemination of knowledge through conferences, articles, training sessions around the tools developed.
Activities • Develop data processing and analysis methods (biomarkers, EEG signals)
• Develop digital calculation and visualization tools
• Exploit the ROMEO supercomputer
• Collaborate closely with the neonatology service of the CHU de Reims
• Participate in the dissemination of knowledge
Competences • Expertise in statistics and data analysis
• Computer skills and programming languages (Python, C/C++, R)
• Knowledge of signal processing
• Knowledge of deep learning methods (GAN, autoencoders, . . . )
• Knowledge of parallelism CPU/GPU (MPI, openmp, cuda/opencl)
• Correct level in English
• Ability to work in a team and independently
• Ability to communicate
Job context
The recruited person will be integrated within the framework of a scientific project carried out in partnership between the Reims Mathematics Laboratory (LMR) UMR CNRS 9008, the CReSTIC EA 3804, and the of neonatology service at the University Hospital of Reims.
This project is focused on the issue of data processing from EEG / aEEG signals, biological data and MRI images for the newborn. It is funded by the National Research Agency and the American Memorial Hospital Foundation.
In this context, the work will consist more precisely in processing and analyzing data from a ancillary study of a cohort of approximately 800 term newborns as part of the LyTONEPAL study one of the objectives of which is to study the predictive factors of unfavorable outcome (neuropathologies, disorders psychomotor) at 3 years.
The work will consist in analyzing predefined biomarkers which will be confronted with characteristics extracted from standard EEG signals using statistical and machine learning tools.
In parallel, it will also be a question of providing treatment and visualization tools adapted to clinical research to determine the neuroprotection measures to put in place, in particular for the management pre-hospital anoxo-ischemic encephalopathies.