Job Type: Engineer in Computer Science (M/W 2 years full time) Location: Nancy, LORIA Lab, Orpailleur (contact: [email protected]) Level: Engineer in Computer Science (Master or Engineering School)

Objectives and Activities. In the framework of the Kesaio Project, a position of engineer in computer science is available for two years in the Orpailleur Team at LORIA Lab in Nancy.

The Kesaio Project is aimed at defining and implementing a semi-automatic methodology for ontology design and extension from available web data (mainly textual data, RDF...).

In this project knowledge engineering is one main topic and will be based on knowledge discovery (mining concept definitions in the web of data), machine learning, knowledge representation, and reasoning. Pattern mining and Formal Concept Analysis (FCA) are planned to be used, together with methods for designing and managing knowledge graphs.

Context of the Engineering Work. This engineering work will take place at the LORIA Lab in the Orpailleur Team (https://orpailleur.loria.fr/). The team has a long and rich experience about knowledge discovery, data mining, knowledge representation, reasoning, and as well in ontology design.

In the Kesaio Project, the Orpailleur Team collaborates with the Airudit Company, head of the project, which develops among others conversational assistants (see https://www.airudit.com/fr-FR/).

Application and competencies. The candidate should have a Master or an Engineering Degree in Computer Science or Applied Maths.

Notions about knowledge discovery, machine learning, knowledge representation, reasoning formalism, and ontology engineering will be highly appreciated.

The candidate should apply on the CNRS portal, the so-called "Portail Emploi CNRS (https://emploi.cnrs.fr/)" and should provide a recent curriculum vitae, a motivation letter, two recommendation letters or the names of two referees, and as well the transcripts of the last three academic years (Bachelor and master Degrees, or Engineering School).

Contact : Amedeo Napoli ([email protected])

Please find attached an offer of Engineer / Researcher in Machine Learning Development, Doctor level (ideally young Doctor in first CDI) on an NLP project within Nunki.co.

Nunki.co is a tech startup specialized in the collection and analysis of data for different sectors (media, security, defense, finance). The mission is interesting because the scope of the substation includes the management of the company's R&D activity on the following topics: NLP / Machine and Deep Learning.

You can find more information in the attachment or at the following link: https://nunkico.notion.site/ CDI-Docteur-en-informatique-ou-Lead-Machine-Learning-Engineer-Startup-Tech-Nunki-co-Paris-St-ba1eb7d188bf4194b02ed983167fbb 9c Location: Station F, Paris or remote work possible Start-up: No later than September 2022

Applications should be sent to Quentin Lhomme at: [email protected] (not .com). Please disseminate this offer to your future ex-doctoral students or young doctors, and more generally in your networks please.

The position is to be filled quickly, do not hesitate to contact me for more information.

A PhD position in Software Engineering and Constraint Programming is open at IMT Atlantique campus Nantes. The PhD contract is fully funded for 3 years and is expected to start in October 2022.

PhD subject: “Automated reconfiguration by AI-augmented model transformation”

Profile: The candidate should have a Master or similar degree in computer science. As the thesis proposal lies at the intersection of software engineering and constraint programming, the candidate should have a strong background in some of these topics.

The PhD student will be supervised by Samir Loudni, Théo Le Calvar and Massimo Tisi at IMT Atlantique in Nantes, France.

Applicant should send us their application before June 26th with: A full curriculum vitae, including a summary of previous research experience A transcript of grades Link to research/development projects A motivation letter discussing how the candidate’s background fits the proposed topic

Two/three support letters of persons that have worked with them The interested applicants can find attached to this mail all the details of the proposition and the information on how to apply. Thank you for distributing this document to your students and colleagues who may wish to distribute it in turn.

Supervisors ● Pr. Samir Loudni, IMT Atlantique, TASC team, [email protected], PhD Director ● Dr. Théo Le Calvar, IMT Atlantique, NaoMod team, [email protected], PhD co-supervisor ● Dr. Massimo Tisi, IMT Atlantique, NaoMod team, [email protected], PhD co-supervisor

A Post-Doctoral position is available as part of a collaboration between the Lirmm laboratory (University of Montpellier/CNRS, France : https://www.lirmm.fr/) and a large French company specialized in the field of mobile application security, .

The Post-Doc position relates to the domain of source code analysis and reverse engineering, the design and production of mobile application analyzer allowing to describe in detail the behavior of an Android and iOS application.

The work of the successful candidate will be to develop features to accurately describe the behavior of an application, designing extensions to support new mobile technology, detection/remediation of security vulnerabilities.

Application requirements: - PhD (or soon to be) in Computer Science and Software Engineering. - Technical background in software engineering and programming. - Technical background in static analysis of source code. - Technical background in architecture and development of mobile applications.

The position is available immediately and applications will be considered until the position is filled. For further information, please contact Dr. Abdelhak-Djamel Seriai, [email protected]

Qualified and interested candidates please email: - Your CV - Abstract of your PhD thesis and two sample research papers

We are looking for a technical consultant to support the deployment of our solutions at our customers.

If you are interested, do not hesitate to apply, the offer is available at this address: https://www.welcometothejungle.com/en/companies/yseop/jobs/consultant-technique_paris?q=eeff2fc1cada931610bcad6ec9798671&o=1113527&e=companies_jobs

*Description of the offer:* As an integral part of the Customer Success team, the technical consultant is at the heart of the solutions we deliver to our customers. He is responsible for maintaining our customers' applications and helps our partners develop new solutions using the SDK.

The Technical Consultant reports to the Head of Customer Success EMEA, based in Neuilly-sur-Seine and joins an international and diverse team of consultant.es, project manager and customer success managers based in Paris and New York.

*The mission of the technical consultant* At the heart of the use of Yseop technology, you participate in the following activities:

- You gradually become a technical referent on one or more existing applications - Maintenance of applications - Addition of new features - Customer support

- You develop applications on the Yseop natural language text generation product (AI/NLG) in relation to our customers: - Participation in the technical framing (definition of the application architecture and technical specifications) - Product configuration and application development (data and knowledge base) - Tests and integration support in connection with the customer

- You contribute to the improvement of the Yseop product: participation in exchanges with the product and R&D teams and writing of expressions of need, internal demonstrations of realized applications , use of new concepts.

- Actor in the development of Yseop, you take part in transversal activities: training, delivery optimization, feedback, etc.

- You have a solid technical background, especially in object-oriented programming (Java), allowing you to master YML, Yseop's proprietary language. You also want to put your skills to good use with cutting-edge AI technology.

*Profile sought* - Engineer or bac + 5 in computer science or TAL / NLP or equivalent training

- Experience in development and object-oriented modeling essential (e.g. Java or Python)

- Knowledge of markup languages (XML, HTML), database management (mySQL in particular) and UML modeling you will help and will be a plus

- Ability to synthesize and take a step back. Intellectual curiosity for the customer's job and technology

- Appetite for artificial intelligence - Appetite for language processing and text generation - Excellent spelling and grammar. We are working on language!

- Fluent English essential - Relational fluency (animation of meetings, daily customer contact)

Cordially, *Dominique Mariko* | Innovation Lab Director yseop.com |

The LabEx EFL is hiring a Post-Doc researcher on the topic of “Distantly Supervised Relation Extraction for Scientific Texts". The post will be supported by the “Laboratoire d’Excellence” Empirical Foundations of Linguistics (LabEx EFL, http://www.labex-efl.org/ ), in the context of an LabEx axis 5 collaboration between the LIPN (http://www-lipn.univ-paris13.fr/>http://lipn.univ-paris13.fr/en/laboratory), RCLN team “Représentation des Connaissances et Langage Naturel” and the ERTIM ”Équipe de Recherche Textes, Informatique, Multilinguisme” (http://www.er-tim.fr) research labs. These partners have already conducted several experiments on unsupervised knowledge extraction from scientific papers [7,8,9]. This post-doc is a follow-up of this collaboration.

Context: Semantic Relation Extraction (RE) is a central task in identifying domain-specific knowledge in text and structuring it into knowledge bases.

In general, a semantic relationship is coded as a triple (entity_1, r, entity_2) where the two entities are linked by a relation r. Currently, most of the systems that are used to carry out this task are based either on unsupervised or supervised paradigms, which have both advantages and disadvantages. Unsupervised methods usually rely on hand-based patterns that may have a very good precision but limited coverage. The patterns themselves could be easier to define for some relations and more difficult for others.

Supervised methods usually obtain a better overall score (in terms of balance between accuracy and coverage) but they require annotated data, which are expensive and slow to produce. In previous work, we explored the scope and advantages of these paradigms [8, 11]. We found that while the two methods have complementary strengths, hybridation techniques allow to improve their performance. These experiments were performed on the ACL-RelAcs [7] corpus of scientific papers in NLP. The dataset was also exploited for a SemEval evaluation campaign in supervised scientific information extraction [10]. A methodology that does not present the problem of manual intervention, either for composing rules or for annotating data, is the so-called Distant Supervision (DS). With DS, any text containing the couple of entities to be linked can constitute a training example [13]. Recently DS has been the focus of various works which highlighted its effectiveness, especially when paired with deep learning methods [14,15,16].

Our research work on relation extraction in scientific text has highlighted the difficulty of the RE task in this specific domain. The difficulties derive from various factors: the fact that entities are not “named entities” like in other Knowledge Bases, the fact that the entities can appear as subject or object in different relations, and the way in which relations are expressed: sometimes these can span various sentences, or be formulated in very different ways. Examples of such relations are “used by”, “applied to”, …, “improves”... etc.

In our previous work [12] we had to combine various extractors to compensate for their deficiencies, taken individually, in order to obtain a good enough accuracy in scientific RE. We believe that Distant Supervision could help to improve the extraction process and eventually replace the ensemble extractors. The PostDoc will review the existing state of the art in the domain of Distantly Supervised Relation Extraction and in collaboration with the team will work towards the definition of a Distantly Supervised methodology for RE in scientific text.

Conditions: Salary between 2100 and 2300€ /month (net)

Selection Criteria: - PhD in Computer Science - Experience and/or interest in: - Natural Language Processing - Text Mining and Machine Learning - Knowledge Engineering, Semantic Web - Good scientific writing skills - Python programming, knowledge of PyTorch

Duration: 12 months (between LIPN and ERTIM) Start: from September 2022

Notice: the first interviews will be carried out on the afternoon of the 29/06/2022

The candidates should send to Davide Buscaldi ([email protected]) and Kata Gábor ([email protected]):

- a detailed CV (with a list of publications) - a cover letter - the names and e-mails of two referees

OPEN POSITION Postdoctoral Researcher in Natural Language Processing and statistical learning for Health

TASKS The main activity of the recruited researcher will be to propose new methodologies and an innovative framework for the development of personalized medicine of low-grade brain tumors based on the latest advances in natural language processing and statistical learning. The research work will rely on textual clinical data and publications. The recruited researcher will also work in collaboration with a recently recruited PhD student working on the extraction of knowledge graphs from publications. He/She may reuse the first prototypes to link the content of clinical reports to this knowledge graph, and thus enrich the structured data produced.

The precise topic within this framework is open to discussion.

TERMS AND TENURE This two-year position will be based at the ATILF, laboratory of Analysis and Computer Processing of the French Language. The duration can not exceed 24 months. The ATILF (https://www.atilf.fr/laboratoire/presentation-english/) is a research unit in (computational) linguistics, including expertise in natural language processing and terminology. Within the framework of multidisciplinary activities, the recruited researcher will be required to interact frequently with two other partner laboratories of the project, the IECL and the CRAN, also located in Nancy, as well as to the neuro-oncology department of the regional hospital (CHRU) of Nancy.

The target start date for the position is October 1, 2022, with some flexibility on the exact start date.

HOW TO APPLY Applicants are requested to submit the following materials: - A cover letter applying for the position - Full CV and list of publications - Academic transcripts (unofficial versions are fine)

Deadline for application is June 30, 2022 . Applicants will be interviewed by an Ad Hoc Commission by July 11-12, 2022. Applications are only accepted through email. All documents must be sent to Mathieu Constant ([email protected]), Marianne Clausel ([email protected]) and Hélène Dumond ([email protected]).

JOB LOCATION Nancy, Lorraine, France

REQUIREMENTS - PhD in natural language processing, computer science, machine learning or applied mathematics (PhD from the University of Lorraine are excluded). - Expert knowledge of Natural language processing

- Good knowledge of statistical learning - Good programming skills - Experience in the interaction with biologists and clinicians - Working in a multidisciplinary team

Within the framework of the ArchivU project, the Labex the Past in the present of the University of Nanterre is recruiting a structural design engineer and analysis of textual data for a period of 12 months in order to work on a diachronic corpus (1971-present) of accounts university council reports:

Call for applications - Recruitment of a research engineer - ArchivU project Pasts in the present (passes-present.eu) http://passes-present.eu/fr/appel-candidatures-recrutement-ingenieure-detudes-projet-archivu-44596

*Context of the research project* Urban modernity is characterized by the conduct of interventions in the city, through voluntary and rational types of action linked to the discipline of urban planning which appeared with the 20th century. Developing the modern city means making its space rational for society that inhabits it.

It thus appears that urban modernity achieves a far-reaching change in societies Western cultures, which establishes in a more specific way a new framework life, breaking with previous periods. In the years that following the Second World War, this project takes on a new magnitude.

If these changes have been studied, this is less the case for their reception and appropriation by city dwellers and the various actors in society. Although decisive, this aspect calls for proof of a renewed approach to identifying and measuring them. It therefore becomes interesting to identify indicators of places and of moments, of events, in which and through which manifest these deep dynamics.

We will focus here on the forms of radio and television expression, which bring to both the discourse (understood in the broad sense of oral, written and visual) of planners and that of the public, in this case the inhabitants and citizens of the modernized city. The partnership offered by the Institute Audiovisual National Authority (INA) thus offers an opportunity exceptional.

To do this, the project plans to identify and analyze the emotions which come under these discourses by using audiovisual sources, but the candidate can suggest other approaches and tools that seem judicious to him.

*Research entrusted to the doctoral student* In partnership with INA, the research aims to develop a tool automatic annotation of emotions recorded in the corpus multimodal projects relating to land use planning and urban planning in using the tools and techniques of Automatic Language Processing (TAL).

The observation of the emotions detected will make it possible to study the perception of territorial development from a new angle. The resources constituted will be used by the INA, and shared and made available to the scientific community and the general public.

*Doctoral student profile* The disciplinary profile of the doctoral student is open to holders of an M2 TAL diploma. An interest in the fields of urban planning and Artificial Intelligence, as well as the experience work with multimodal corpora will be appreciated.

A good command of the French language is expected.

* CALENDAR* - Publication of the call for applications: Thursday, June 9, 2022

- Deadline for submitting applications: Friday, August 29, 2022 at 5:00 p.m. (Paris time) - Preselection of applications: between August 30 and September 2

- Announcement of the pre-selection of candidates: September 5, 2022

- Hearings: September 8 and 9, 2022 - Announcement of results: mid-September 2022

- Starting position: October 1, 2022 *INFORMATION CONCERNING THE DOCTORAL CONTRACT*

- Duration: 3 years; - Start of the contract: academic year 2022-2023;

- Remuneration: gross monthly flat rate: €1866/month (remuneration in valid at the time of publication of the announcement). Missions additional teaching will be possible, according to the rules in force at the University where the doctoral student will be registered.

*ELIGIBILITY CRITERIA* - Students who have not yet registered can apply in thesis and who have defended their Master 2 dissertation on the date of submission of applications.

- Candidates may be students who have completed their university course in France or abroad.

*SELECTION CRITERIA * The selection criteria for this doctoral contract will be the following:

- the scientific quality of the dossier (clarity of the problem, methodology and methods of implementation); - the quality of the candidate's career;

- the match between the candidate, his/her thesis project and the profile of the “VITAL” doctoral contract; - knowledge and mastery of learning tools automatic (machine learning, deep learning) and annotation manual;

- the ability to work in a group with specialists from other disciplines; - interest in the field of town planning and the development of space in general as well as for multimodal data.

*SELECTION METHODS* The judging panel will be made up of the following people:

- 1 external member mandated by the Labex Past in the present; - 1 member representing INA; - The scientific manager of the Labex Past in the present or the person authorized to represent him;

- 1 member mandated by the ComUE UPL Doctoral College (one of the two co-directors); - a representative of the ComUE presidency team Université Paris Lumières, which chairs the jury.

Hearings are scheduled in person at the headquarters of the University of Paris Lights, 75013 PARIS. This method may be modified depending on ministerial directives related to the COVID19 pandemic.

Candidates who wish to apply for this doctoral contract must provide a file consisting of the following documents:

1. The completed application file (including the thesis project: 10,000 characters maximum. Font Time, size 12, bibliography summary included. Please note: for the sake of equality, the pages will be removed from the file);

2. An academic curriculum vitae (2 pages maximum); 3. A cover letter (3,000 characters maximum);

4. Master's transcript; 5. The master's degree or, failing that, the defense certificate; 6. The Master's thesis (in .pdf);

7. The reasoned opinion of the thesis supervisor anticipated; 8. The reasoned opinion of the management of the host research unit.

The application file will be sent in electronic form in the format .pdf (a *single file* bearing the name of the candidate) on the address following: *pasp.allocations22[at]passes-present.eu*

In the "subject" of this email, please specify expressly as according to the following example: /Candidature-DOC-VITAL/

An acknowledgment of receipt will be sent thereafter.

For any additional information about this call for applications (outside definition of the scientific project of the thesis), write to the following address: *pasp.allocations22[at]passes-present.eu*

*To contact the scientific team of the VITAL* project, please contact: Iris ESHKOL-TARAVELLA: ieshkolt[at]parisnanterre.fr

Olivier RATOUIS: oratouis[at]parisnanterre.fr

*To contact INA: *contact Géraldine POELS: gpoels[at]ina.fr

*Context* As part of a project carried out in 2020-2021, ERTIM carried out with EDF R&D an experimental study on the use of a system of automatic detection of opinions and arguments in texts relating to the theme of the electric vehicle. The study carried out makes account of the difficulties of this task, in particular on the preparation of datasets (by manual annotation) as well as the performance (precision / recall) of the algorithms.

This collaboration is continuing, the first objective being to increase and to diversify the dataset, in order to experiment with techniques more robust and recent. The work will begin with a detailed analysis, quantitative and qualitative, of the results previously obtained, as well as than through the use of other analysis tools (in particular, patterns and parsers). The corpus will be extended to a new thematic (nuclear) and a greater quantity of data will be annotated.

Other state-of-the-art methods (deep learning and patterns) will be tested and evaluated on the dataset created. A method will then be developed to merge the arguments excerpts, using textual similarity measures, and as far as possible the links between groups of arguments (opposing, support, etc.) in order to establish an interpretable map in the form argument network.

*Missions* The recruited engineer will be in charge of the following missions: - familiarization with the existing prototype, quantitative analysis and qualitative results,

- increase of the data set by manual means or semi-automatic, - exploration of alternative methods (deep learning, patterns),

- analysis of results and prospects for improvement, - experimentation of argument merging algorithms by similarities, - development of a web interface for viewing arguments and their relationships.

*Required profile* - Diploma in NLP (or computer science) - Python programming skills - Good knowledge of annotation methodologies and model learning

- Good understanding of qualitative evaluation measures and quantitative - Knowledge of (or interest in) interface development web visualization

*Frame* - Contract: 6-month full-time engineer contract - Start date: as soon as possible - Remuneration: from 1600€ to 2200€ net according to experience - Location: Inalco research center, 2 rue de Lille, 75007 Paris

*Candidacy* Please send your CV and share your motivations to Damien Nouvel ([email protected]) and Ilaine Wang ([email protected])

I am disseminating this thesis offer from my colleagues at CEA Marcoule who are looking for a student with an "Artificial Intelligence" profile for the attached topic: Construction of a generic digital twin of vitrification processes for the production of virtual tests.

The desired start date is September 2022 but can be postponed until the end of 2022.

The main contact for the subject is Caroline CHABAL: [email protected].

PhD Position (CIFRE): Relational query optimization for multidimensional data Offre de thèse (CIFRE): Optimisation de requêtes relationnelles pour les données multidimensionnelles

Context Query performance on analytic workloads is heavily influenced by the quality of the query optimizer-one which has prompted several decades of research and advancement on the different components of the query optimizer. When it comes to query optimization and plan cost estimation, no one-size fits all [2]. This is usually because some relational operators and their corresponding enumeration algorithms are often vendor-specific, complexity of the analytic workload, and the underlying system architecture are also some contributing factors.

Multidimensional data models are very popular in scientific processing and machine learning workloads [3] as they help capture variable data. Some of the prevalent data types include arrays and dictionaries or maps and are also now becoming a serious part of financial workload. Query evaluation on multidimensional workloads often involves multiple levels of aggregation over these sets of data. A relevant work in this sphere described in [4] provides a technique for lazily evaluating aggregates by fusing group by and join operator. In [5], the authors proposed a set of low-level plan operators for SQL-style statistical expressions that modularizes aggregate implementations whenever multiple aggregates are combined.

Work in this area provides the foundation for query evaluation on multidimensional data but additional research that focuses on the application of more robust optimization techniques and their cost analysis are the goal of this research.

Research Objectives The primary focus of this work is to develop optimization techniques for queries on multidimensional workload. During the first phase of this research, the candidate will conduct a thorough state-of-art study that focuses on cost model and optimization techniques applicable to this domain. In the next phase, an investigation of query constructs that are a source of bottleneck for query evaluation will be studied. This means in particular identifying the most important logical constructs, and their frequent combinations in practice that are the most interesting to optimize.

Candidate’s Profile The ideal candidate for this role must possess an MSc in Computer Science or closely related fields. The candidate must be proactive and highly-motivated to carry out advance research, and with a well-developed analytical problem-solving ability. Good understanding of functional programming in Scala (or willingness to learn) is required.

About the Team This research will be carried out between Opensee and Tyrex team at Inria Grenoble Rhône-Alpes.

Opensee (opensee.io) is a fintech company with headquarters in Paris, offering instant and self-service analytics to financial institutions, helping them better respond to regulatory and business requirements and turn their big data challenges into competitive advantage. The Core Engine team at Opensee, research, experiment, maintain, and develop features for the query engine which is at the core of data processing within Opensee.

The Tyrex team (tyrex.inria.fr) is affiliated with CNRS LIG, Inria, UGA, and Grenoble INP; and located in Montbonnot near Grenoble in France. The Tyrex research group focuses on the foundations of the next generation of data analytics and data-centric programming systems and has produced many outputs in research and industrial applications. The candidate will graduate from the University of Grenoble Alpes (UGA).

Type of position: 3 years contract.

How to apply The candidate should send an email to Pierre Genevès ([email protected]) and Nabil Layaïda ([email protected]) with an application file composed of:

- a detailed CV - a motivation letter - academic transcript (relevé de notes)

We invite applications for a 3-year PhD position at the University of Lille in the context of the recently funded research project "COMANCHE" (Computational Models of Lexical Meaning and Change). The position is funded by Inria, the French national research institute in Computer Science and Applied Mathematics.

COMANCHE proposes to transfer and adapt neural word embeddings algorithms to model the acquisition and evolution of word meaning, by comparing them with linguistic theories on language acquisition and language evolution.

At the intersection between Natural Language Processing, psycholinguistics and historical linguistics, this project intends to validate or revise some of these theories, while also developing computational models that are less data hungry and computationally intensive as they exploit new inductive biases inspired by these disciplines.

The first strand of the project, on which the successful candidate will work, focuses on the development of computational models of semantic memory and its acquisition. Two main research directions will be pursued. On the one hand, we will compare the structural properties associated to different semantic spaces derived from word embedding algorithms to those found in human semantic memory as reflected in behavioral data (such as typicality norms) as well as brain imaging data.

The latter data will then used as additional supervision to inject more hierarchical structure into the learned semantic spaces. One the other hand, we intend to experiment with training regimes for word embedding algorithms that are closer to those of humans when they acquire language, controlling the quantity as well as the linguistic complexity of the inputs fed to the learning algorithms through the use of longitudinal and child directed speech corpora (e.g., CHILDES, Colaje). In both cases, both English and French data will be considered.

The successful candidate holds a Master's degree in computational linguistics or computer science or cognitive science and has prior experience in word embedding models. Furthermore, the candidate will provide strong programming skills, expertise in machine learning approaches and is eager to work across languages.

The position is affiliated with the MAGNET team at Inria, Lille [1] as well as with the SCALAB group at University of Lille [2] in an effort to strenghten collaborations between these two groups, and ultimately foster cross-fertilizations between Natural Language Processing and Psycholinguistics.

Applications will be considered until the position is filled. However, you are encouraged to apply early as we shall start processing the applications as and when they are received. Applications, written in English or French, should include a brief cover letter with research interests and vision, a CV (including your contact address, work experience, publications), and contact information for at least 2 referees. Applications (and questions) should be sent to Angèle Brunellière ([email protected]) and Pascal Denis ([email protected]).

The starting date of the position is 1 October 2022 or soon thereafter, for a total of 3 full years.