Detailed description in English: https://selexini.lis-lab.fr/jo bs/2022/03/29/engineer-position
- Duration: 12 months
- Start: June 2022 (adaptable)
- Application: before May 2, 2022 by email to carlos.ramisch [AT] lis-lab.fr
- Location: LIS (http://www.lis-lab.fr/), TALEP (https://talep.lis-lab.fr/), Aix Marseille University (https://www.univ-amu.fr/), Luminy campus (https://sciences.univ-amu. fr/sites-geographiques/site-luminy), Marseille - Remuneration (CDD): €1,600 to €2,000, depending on experience
The objective of the ANR project *SELEXINI (https://selexini.lis-lab.fr/)* is to develop original methods of *lexicon induction* in automatic language processing The lexicons produced by *clustering * will bring together occurrences of words according to their meanings, but will also contain polylexical expressions, semantic *frames* , argumental structure, generated definitions, etc. Lexicon induction methods will rely on neural language models (e.g. FlauBERT, CamemBERT) and existing lexical resources (e.g. Wiktionary).
The i*ngénieur recruited will be responsible for setting up the initial infrastructure of the project*, both in terms of data and tools. The mission will take place in 5 stages: (1) preparation of a large raw corpus representative of various registers of written French, (2) pre-processing of the corpus using parsers, etc., (3) extraction and structuring of French Wiktionary entries, (4) adaptation (*fine-tuning*) of language models on the project corpus, (5) alignment of words and expressions polylexicals extracted from Wiktionary with occurrences of the corpus.
### Profile - Master or thesis in a field related to automatic language processing - Notions of French and English - Interest in languages and familiarity with language technologies ### Application
Send your CV and a few lines explaining why you are applying to carlos.ramisch [AT] lis-lab.fr before May 2, 2022.
The ENP-China research team is looking for an engineer in TAL. The ERC ENP-China (https://www.enpchina.eu) project is a research project in "Digital Humanities" that aims to study the mutation of elites in China between the 19th and 20th centuries on the basis of a large amount of data, especially textual, in Chinese and English of this period. The automatic identification of individuals and organizations/institutions mentioned in historical texts -- especially in the press -- is essential to process the volume of data available.
Different named entity recognition models have already been adapted within our team and are actively used on our data. However, this approach does not allow for the precise identification of the extracted entities.
The work of the recruited engineer will focus on the development of a system of binding named entities to link named entities found in historical texts with different knowledge bases: MCDB (https://bookdown.enpchina.eu/ mcbd_usermanual/), Wikidata, Baidu Baike... In addition, it will also be necessary to link (with a unique identifier) entries in different knowledge bases about the same people or organizations. In addition, other NLP tasks will be implemented, such as re-segmentation and/or classification of Articles in the English and Chinese press .
# The position - Contract: full-time fixed-term contract of 6 months (which can be extended) - Start date: As soon as possible - Gross salary: 3100€ per month - Location: Aix-en-Provence The employer is Protisvalor / Aix-Marseille University
# Missions - Set up a system of binding of named entities adapted to our databases and knowledge. - Integrate this system with our existing infrastructure (in collaboration with other team members). - Set up an article segmentation system adapted to the needs and data of historians. - Assist and advise team historians in order to solve problems where NLP could be a solution.
# Qualifications and Requirements - Have a Master's degree/Engineering degree in NLP or Machine Learning . - Good Python skills and good knowledge of commonly used NLP/ML libraries (PyTorch and/or Tensorflow, SpaCy, Transformers, ...).
- A good command of Linux is required. - A good command of English is required. French and Chinese are not required. - Ability to organize effectively and work in an internationally oriented team .
# Application Send CV and cover letter by email to [email protected] and [email protected]
Duration: 24 months
Desired hiring date: ASAP (may be modified according to the sanitary situation) Take-home salary: 2600 € / month (French public healthcare coverage included)
Workplace: the L3i laboratory in La Rochelle, France
Specialities: Machine Learning / Image Analysis / Computer Vision /
Description of the Lab: The work carried out by the candidate will be part of a joint project between the L3i laboratory and IMDS company. This project is funded by the “Plan de Relance” from France and European Union.
The post-doc fellow will be based in the L3i Laboratory, located La Rochelle, France. The L3i laboratory, created in 1993 at La Rochelle University brings together researchers in Computer Science and Signal Processing from different faculties. The L3i brings together the skills of its researchers in order to address the issues of digital content enhancement from a systemic perspective.
This relies, in particular, on a cross exploitation of interactive applications, content indexing and knowledge representation. The laboratory is structured around three scientific themes (Knowledge Engineering, Content Analysis and Management, Interactivity and Dynamic Systems), centred on the common goal of interactive and intelligent management of digital content. The mission of the job will also be conducted in coordination with IMDS, present in France and Canada.
IMDS offers a complete range of services, from consulting to operational validation, including software and hardware prescription, technology deployment, staff training and solution operation. Based on the innovative concept of the "Advanced Document", IMDS develops production architectures that cover the entire document life cycle, from the application data to the physical or electronic distribution of the document produced.
Job description: The work of the post-doc fellow will fall within the framework of the area "Control and assessment of a capture quality".
The aim is to design innovative approaches for the evaluation and the increase of the quality of ID Documents captures (ID Card, driving licence, passport, others) in order to improve the verification of the validity of the document in an authentication context while a distant digital relationship (connection to a bank or administrative service for instance). Where recent mobile devices already perform correct image processing on photos in general, it is less the case when capturing documents in deteriorate conditions (movement, lack of light).
More, the required quality of the capture is higher than standard when the aim isto control the veracity of the document.
There are many scientific bottlenecks arising from this applicative context, mainly in the field of machine learning and pattern recognition.
Using video capture seems to be a promising solution to address glare issues and secure the authentication process This post-doctoral work will be based on a detailed state of the art of existing approaches, to identify their limits and propose innovative approaches that will help to overcome the bottlenecks mentioned above. To solve these problems, we plan to propose new techniques.
For instance, many quality assessment focuses on simple criteria such as read rates. It seems important to take into account the constraints and results of additional more advanced controls like textures or graphics. A competition of all approaches (deep-learning or not) should be developed to fit some real world constraint like time processing and capacity to run on mobile.
Candidate Profile: The candidate, who holds a Ph.D. in the fields of computer science, computer engineering, signal processing, or applied mathematics, must have a significant research experience in at least two of the following areas:
• Machine learning • Pattern recognition • Computer Vision OR image processing (knowledge and/or experience in both domains would be considered a plus for the applicant)
The candidate's skills will include: • Mastering one or more programming languages (Java, Python, C/C++...) • Very good teamwork skills, having knowledge or experience of Agile methods would be a plus (the work will be carried out both in conjunction with researchers from the L3i laboratory and the R&D department of the IMDS company) • Good scientific writing skills, and fluency in writing and speaking English
To apply: Candidates for this position should send a CV and a cover letter (names and reference details would be appreciated) to: [email protected] [email protected] [email protected]
Applications will be considered as they arise and will be closed by the 13th of April
Robot vision engineer/ sr. Engineer job position at Hand Plus Robotics Pte Ltd, Singapore.
In this role you will be working on robotic fulfillment technologies - like pick, place, pack, sort, palletize, depalletize etc. Hand plus is pioneering novel robotic systems for automating the material handling in warehouses and the hospitality sector. Joining this team enables you to be on the forefront of new product development and cutting edge technology that improves our systems and the experience for our customers.
We are looking for an experienced, truly innovative scientist/ engineer to explore and develop solutions for object detection, segmentation of cluttered objects and pick pose estimation problems. We expect the candidate to be handson with 3D cameras, deep learning, ROS/openCV libraries and well versed in python and c++.
Come join us to experience an exciting career.
Candidates must have an MSc or engineering degree in a field related to computer science, electrical engineering, or applied mathematics, with strong programming skills (in particular with deep learning frameworks). Experience with ROS and robotic arm will be a plus. Candidates are expected to have good communication skills and be a team player.
The position is starting as soon as possible. Salary is according to the market standard in Singapore and is negotiable.
Applications should include the following documents in electronic format: i) A detailed CV ii) The transcripts for your degrees. iii) The contact information for at least one reference (do not include the reference letters with your applications as we will only ask for the reference letters for short-listed candidates). Selection process includes - initial screening based on the application, 1 round of technical interview followed by a HR interview.
A CIFRE thesis project will be submitted to ANRT by Airbus Helicopter (Marignane) and the CLLE laboratory (Cognition, Languages, Language, Ergonomics, Toulouse). It concerns "The evaluation of the contribution of multimodality to a controlled language optimized for a fair understanding of maintenance instructions by users (m/f)".*
*The thesis has two objectives:*
- To analyze the contribution of multimodality to the controlled language in English
- To explore visual, audio, verbal and new technologies to determine how they complement or replace helicopter maintenance instructions and the contribution of each of them*.*
The PhD student will build on existing work in the field of controlled languages and multimodality. The doctoral student's proposals, based on linguistic knowledge and cognitive ergonomics, will be evaluated by users of the documentation in order to measure the effectiveness of the messages.
In a second step, the PhD student will make recommendations of appropriate semiotic formats, based on the results of the evaluations , to ensure the best possible understanding of the maintenance instructions by users. He/she will take into account their characteristics (qualifications, multilingual and multicultural profile), their objectives, environment and work constraints.
https://ag.wd3.myworkdayjobs.c om/fr-FR/Airbus/job/Marseille-Area/Thse-Cifre---Human-factors--H-F-_JR10107347
For any request for clarification, please contact:
The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is looking to recruit a PhD student starting on October 1st 2022 on the following subject : Spatio-temporal data augmentation models for motion pattern learning using deep learning: applications to facial analysis in the wild
The FOX research group is part of the CRIStAL laboratory (University of Lille, CNRS), located in Lille, France. We focus on video analysis for human behavior understanding. Specifically, we develop spatio-temporal models of motions for tasks such as abnormal event detection, emotion recognition, and face alignment. Our work is published in major journals (Pattern Recognition, IEEE Trans. on Affective Computing) and conferences (WACV, IJCNN).
This PHD thesis will be funded in the framework of the AI_PhD@Lilleprogram. http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/
The candidate will be funded for 3 years; he/she is expected to defend his/her thesis and graduate by the end of the contract. The monthly net salary is around 1800€, including benefits (health insurance, retirement fund, and paid vacations).
The position is located in Lille, France. With over 110 000 students, the metropolitan area of Lille is one France's top education student cities. The European Doctoral College Lille Nord-Pas de Calais is headquartered in Lille Metropole and includes 3,000 PhD Doctorate students supported by university research laboratories. Lille has a convenient location in the European high-speed rail network. It lies on the Eurostar line to London (1:20 hour journey). The French TGV network also puts it only 1 hour from Paris, 35 mn from Brussels, and a short trips to other major centres in France such as Paris, Marseille and Lyon.
Abstract: Facial expression analysis is a well-studied field when dealing with segmented and constrained data captured in lab conditions. However, many challenges must still be addressed for building in-the-wild solutions that account for various motion intensities, strong head movements during expressions, the spotting of the subsequence containing the expression, partially occluded faces, etc. In recent years, learned features based on deep learning architectures were proposed in order to deal with these challenges. Deep learning is characterized by neural architectures that depend on a huge number of parameters. The convergence of these neural networks and the estimation of optimal parameters require large amounts of training data, especially when dealing with spatio-temporal data, particulary adequate for facial expression recognition. The quantity, but also the quality, of the data and its capacity to reflect the addressed challenges are key elements for training properly the networks. Augmenting the data artificially in an intelligent and controlled way is an interesting solution. The augmentation techniques identified in the literature are mainly focused on image augmentation and consist of scaling, rotation, and flipping operations, or they make use of more complex adversarial training. These techniques can be applied at the frame level, but there is a need for sequence level augmentation in order to better control the augmentation process and ensure the absence of temporal artifacts that might bias the learning process. The generation of dynamic frontal facial expressions has already been addressed in the literature. The goal of this Ph.D. is to conceive new space-time augmentation methods for unconstrained facial analysis (involving head movements, occultations, etc.). Attention should be paid in assessing the quality standards related to facial expression requirements: stability over time, absence of facial artifacts, etc. More specifically, the Ph.D. candidate is expected to conceive augmentation architectures that address various challenges (motion intensities, head movements) while maintaining temporal stability and eliminating facial artifacts.
More details are available here : https://bit.ly/staugm_motion
Candidates must hold a Master degree in Computer Science, Statistics, Applied Mathematics or a related field. Experience in one or more of the following is a plus: • image processing, computer vision; • machine learning; • research methodology (literature review, experimentation…).
Candidates should have the following skills: • good proficiency in English, both spoken and written; • scientific writing; • programming (experience in C++ is a plus, but not mandatory).
We look forward to receiving your application as soon as possible
PhD. position in Computer Science Machine learning and data mining with imperfect and incomplete relational data Laboratoire Hubert Curien (LabHC), France
Title: Complex graph data analysis with incomplete / incomplete data
Laboratory: Laboratoire Hubert Curien (LabHC UMR CNRS 5516), Université Jean Monnet, Saint-Étienne, France
Keywords: Data mining, Machine learning, Social network, Graphs analysis, Incomplete / imperfect data
General Overview: In many applications, the data to be studied is relational, modeled in the form of a network represented by graphs. This representation allows capturing not only the information about entities (using attributes or properties) but also the relationships between them.
While the ability to discover knowledge from such network data is gaining in importance, the quality became a central issue in their exploitation. The aim of this PhD is first to study the impact of the lack of quality of relational data on the data mining and machine learning algorithms and, second to design robust methods to deal with imperfect and incomplete relational data and able to provide explainable results
Working environment: The PhD candidate will work at the Laboratoire Hubert Curien (UMR 5516) under the supervision of Baptiste Jeudy, Charlotte Laclau and Christine Largeron, (LabHC – Université Jean Monnet, Saint-Etienne, France).
Funding: The PhD fellowship is funded for 3 years from October 2022 and is monthly funded about approximatively 1500 €.
Profile of the candidate: The candidate should have a master degree or equivalent in Computer Science. The subject is at the intersection of several domains: graph theory, statistics, data mining and machine learning. Thus the candidate should have strong backgrounds in several of these topics.
Other required skills: • Good abilities in algorithm design and programming. • Good technical skills regarding data mining, machine learning and data management • A very good level (written and oral) in English. • Good communication skills (oral and written). • Ability to work in a team with colleagues, • Autonomy and motivation for research.
Application instructions: Applicants are invited to contact as soon as possible. The application file should contain the following documents:
1. a curriculum vitæ (CV);
2. the official academic transcripts of all the candidate’s higher education degrees (BSc, License, MSc, Master’s degree, Engineer degree, etc.). If the candidate is currently finishing a Master’s degree, s/he must send the transcript of the grades obtained so far, with the rank among her/his peers, and the list of classes taken during the last year;
3. some recommendation letters (quality is more important than quantity, there);
4. and a motivation letter written specifically for this position.
Send all of these documents by email to all the advisors: • Baptiste Jeudy [email protected] • Charlotte Laclau [email protected] • Christine Largeron [email protected]
Interviews will be conducted as they arise and the position will be filled as soon as possible
Important remark: This internship is part of an international collaboration between the Alberta Machine Intelligence Institute (AMII) at the University of Alberta in Edmonton and the LabHC notably within the framework of the IEA CODANA supported by the CNRS. As part of the thesis, the doctoral student could stay at the AMII at the University of Alberta in Edmonton (Canada).
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
**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/ )
**Duration** 4 years
**Location** CNRS@CREATE Singapore (https://www.cnrsatcreate.cnrs.fr/about-us/)
**Stipend ** 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 15th 2022
**How to apply** Please send your CV with transcripts to Jian Su [email protected], Joty Shafiq Rayhan [email protected] and Farah Benamara, [email protected].
Location: KU Leuven, Leuven, Belgium
Starting Date: Summer/fall 2022
Duration: 4Y
Supervisors: Profs. Renaud Detry and Herman Bruyninckx
Announcement Posted On: April 4 2022
Status and additional information: http://renaud-detry.net/jobs/vac/kuleuven-eu-2022a.php
## Research KU Leuven looking for two highly motivated PhD students for an EU-funded project in construction robotics. The aims of the research are to develop methods that allow a robot to maintain situational awareness and conduct dexterous manipulation tasks despite high perceptual uncertainty caused by the presence of dust in the air. One student will be responsible for learning-based mechanisms that allow the robot to fuse data from multiple sensors and forward models of its own progress to solve a situation-awareness problem. The second student will focus on force-controlled manipulation of compliant material.
Funding is available for four years. KU Leuven is Belgium's largest university, and a leading higher education and research institution – amongst the top-100 universities worldwide. It is located in Leuven, a short train hop away from Brussels. This project will be hosted by two departments: the departments of electrical engineering (group PSI) and mechanical engineering (group RAM).
## TA Work The successful candidates will assist with teaching at most one EE/ME course per quad (i.e., two per year). The workload will be at most 6 hours per week.
## Requirements The successful candidates must have a degree(s) in Engineering, Physics, Math, Computer Science or a related field, and fluency in at least one mainstream computer programming language.
Candidates must demonstrate their ability to assist with the TA work, either with a degree or transcripts of courses relevant to EE or ME.
For the first task (student 1), a machine-learning background is strongly valued.
## Application Applicants must submit: - a one-page cover letter describing their background and interests,
- curriculum vitae, including publications, and a list of in-person or online courses on computer vision, machine learning and robotics that the applicant has completed,
- availability (earliest feasible starting date),
- if available, a link to the candidate's SCM page (e.g., on GitHub.com) that illustrates past programming projects,
- a copy of academic transcripts (bachelor/master grades).
Applicants must be prepared to provide two reference letters upon request.
Applications should be sent, *in a single PDF document*, to:
Questions can be directed to the same address.
Applications can be sent immediately and will be evaluated until the position is taken. To verify if the position is still available, visit http://renaud-detry.net/jobs/vac/kuleuven-eu-2022a.php. End of cvml Digest Wed, 06 Apr 2022
Hi all The AiBy4 project (https://aiby4.ls2n.fr) proposes several thesis topics in interdisciplinary collaborations AI and health or industry of the future. Several laboratories are concerned: LS2N, CRCINA, GEM, DCS...
Keywords: Deep learning, medical imaging, natural language processing, signal analysis and processing, logic, occupational health, digital twins ...
The list of topics is available online.
https://aiby4.ls2n.fr/2022/03/25/sujet-de-these-2022/
Application deadline: end of April
Contact the leaders of each project directly.
TEAM Title of the thesis project: Automatic Classifiers of Ontologically Explainable Images: application to the Observation and Analysis of Marine Flying Fauna.
Keywords: Artificial Intelligence, Computer Vision, Knowledge Engineering, Explainability of AI
Funding: Hauts de France Region / ULCO (Thesis scholarship)
Contact: [email protected], mourad.bouneffa@univ-littoral. En
Summary : Responding to the need for explainability of AIs that use Deep Learning, we propose in this thesis to develop a hybrid approach in Computer Vision for the automatic classification of images combining machine learning models with semantic knowledge expressed by ontologies, with the aim of creating ontologically explainable image classifiers. The SysReIC team has already achieved very encouraging results in a prototype. This thesis will consist in developing these ideas by applying them to the ecological problem of monitoring and observing the trajectories of flying fauna in partnership with the companies Écosphère (1, Antenne Nord Littoral) and Prodrone (2). This type of study is often a prerequisite for impact analysis for the installation of infrastructure both at sea (offshore platforms, ...) and on land (industrial installations, bridges, ...). This process is now carried out by overflight campaigns of sites studied by ornithologists in aircraft. This approach is costly, dangerous, and moderately effective. To overcome these problems, it is proposed to implement a new process using autonomous flying image capture devices (wings and drones), and to carry out image analysis using AI techniques. In this context, the importance of the issues requires the implementation of the expert knowledge necessary to refine the analysis process, but also to provide results that are explainable to specialists.
(1) https://www.ecosphere.fr/ (2) https://www.prodrones.fr/
Hello We offer a thesis offer within the Models and Knowledge team at the L3i laboratory in La Rochelle.
This thesis aims to analyze complex and heterogeneous data for the adaptation of robotic scenarios with the consideration of all dimensions of interaction.
The thesis will start in October 2022 for a period of three years.
You can find all the information of this offer on the laboratory website.
https://l3i.univ-larochelle.fr/spip.php?action=acceder_document&arg=1677&cle=165473a0a3d6bc24df3c4d7aabfcd6ac658b8a5f&file=pdf/sujet_bertet_mondou_cle81be17.pdf
Deadline : 31 may
Full-time PhD position in AI -- Federated Learning using Inductive Logic Programming (ARIAC project) University faculty : Computer science
Grade : researcher
Contract : renewable fixed term contract
Category : scientific personnel
Allocation : External funds
Job description The proposed research posittion is aimed at investigating fundamental research in the context of federated learning using inductive logic programming. The position is tailored for a researcher willing to do a PhD thesis. It is conceived as a 4-years program. It is offered as a 2-years position, renewable for up to 2 additional years. The person hired will join the Focus research center under the guidance of Prof. J.-M. Jacquet, I. Linden and W. Vanhoof. It is expected that the researcher will tackle both the design of languages and the study of their semantics. This project will also be conducted with the academic and industrial partners of the ARIAC project. More information on the project can be found at https://staff.info.unamur.be/jmj/filp.
Context ARIAC by DigitalWallonia4.ai is a research project funded by the Walloon Region bringing together the five French-speaking universities and four Walloon research centers with the primary objective of accelerating the development of artificial intelligence technologies in Wallonia. The project is part of the TRAIL (Trusted AI Labs) initiative, launched in September 2020, which aims at enhancing the development of artificial intelligence technologies in Wallonia. Among the different axes, the ARIAC project aims to develop Trusted Mechanisms for AI in the goal of offering guarantees to people using AI techniques. In that context, we explore the potential of inductive logic programming in a federated learning scheme. More information on TRAIL can be found at https://trail.bydw.be/en.
About the employer The University of Namur (UNamur) is located in the center of Belgium, in the French-speaking part of the country. It offers quality education to more than 7,000 students every year and hosts more than 900 researchers in all fields of expertise. The Faculty of Computer Science provides cutting-edge teaching and research, intending to put computers at the service of society, taking account of their impact on the environment and respecting the values of solidarity and sustainable development. The Faculty of Computer Science is a founding member of the Namur Digital Institute (NADI), which groups together over 150 researchers in digital technology. It has a multi-disciplinary approach and addresses, in particular, the issues and challenges of computer science in organizations and society. The Faculty of Computer Science has over 400 students, 80 members of staff, including 18 professors and around 50 researchers. Founded in 1968, the Faculty has trained over 1,800 high-level computer science graduates since then.
Profile Those who apply will carry: a Master's degree (120 ECTS credits) in Computer Science, or equivalent, or a Civil Engineering degree, a Business Engineering degree or a Master in Mathematics with solid knowledge of computer-science related topics, or equivalent
The person hired will demonstrate: interest for language design, semantics and declarative programming ability to integrate and work in a research team, good communication and presentation skills in English (read, written, spoken), knowledge of French is considered as a plus, sense of initiative and responsibility, autonomy, and organization.
Note that soon to be graduating master students are welcome to apply provided that they will have graduated before the start of the position. Candidates that already hold a PhD degree are not eligible.
Additional information For additional information please do not hesitate to contact one of the project leaders : J.-M. Jacquet, I. Linden or W. Vanhoof (see email addresses below).
How to apply? Applications should be sent by e-mail to [email protected] AND [email protected] AND [email protected] and contain the following:
a motivation letter describing the interest in the research topic,
a recent CV,
a copy of diplomas (Bachelor and Master, if available),
a transcript with the grades obtained for each course taken on each university year, the name and e-mail address of two reference persons to be contacted upon request.
A dedicated selection committee will examine applications.
Important dates Submission deadline: May 15th, 2022 (11h59 pm AoE).
Expected starting date: September 1st, 2022 (negotiable, depending on COVID or candidate constraints).