法國斯特拉斯堡大學(xué)2023年招聘博士后（用于水生態(tài)系統(tǒng)恢復(fù)決策支持的異構(gòu)信息的表征、查詢和調(diào)整）
斯特拉斯堡大學(xué)（法: Université de Strasbourg；德：Universität Straßburg；英：University of Strasbourg），簡稱UDS或Unistra，位于法國阿爾薩斯大區(qū)下萊茵省斯特拉斯堡市，是一所綜合性大學(xué)，法國卓越大學(xué)計(jì)劃高校。
學(xué)校成立于1538年，1971年因法國五月風(fēng)暴影響拆分為三個(gè)獨(dú)立的大學(xué)，于2009年1月1日重新整合為現(xiàn)在的斯特拉斯堡大學(xué)。斯特拉斯堡大學(xué)在很多領(lǐng)域都享有盛名，擁有18名諾貝爾獎(jiǎng)得主，1名菲爾茲數(shù)學(xué)獎(jiǎng)得主。
Post Doctoral Position - Representation, Interrogation And Adaptation Of Heterogeneous Information For Decision Support In Hydro-Ecosystem Restoration
Universities and Institutes of France
France
October 12, 2023
Contact:N/A
Offerd Salary:Negotiation
Location:N/A
Working address:N/A
Contract Type:Other
Working Time:Full time
Working type:N/A
Ref info:N/A
17 Aug 2023
Job Information
Organisation/Company
Université de Strasbourg
Department
Direction de la Recherche
Research Field
Computer science
Researcher Profile
Recognised Researcher (R2)
Country
France
Application Deadline
12 Oct 2023 - 23:59 (Europe/Paris)
Type of Contract
Temporary
Job Status
Full-time
Offer Starting Date
15 Nov 2023
Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
1. Position identification
Contract/project period: 20 months Expected date of employment: 15/11/23
Proportion of work: 100 %
Workplace: Strasbourg, France (ICube lab)
Desired level of education: PhD in computer science
Experience required:
Contact(s) for information on the position (identity, position, e-mail address, telephone):
Florence Le Ber, Dr. HDR, ICube, florence.leber@engees.unistra.fr
Date of publication: 17/08/23
Closing date for the receipt of applications: 12/10/23
2. Research project or operation
The German-French project TETRA focuses on the development of an AI based toolbox and methodology for the water domain. This project will begin in June 2023 until June 2026 (3 years). Four partners are involved, two German et two French partners, two private compagnies (SEBA, Thalès) and two public research centers (Fraunhofer IOSB, ICube). The project is organized into several work packages. This postdoctoral position is concerned by one of the work packages (WP6): its aim is to develop a decision support system exploiting feedbacks from hydro-ecosystem restoration operations.
The restoration or renaturation of hydro-ecosystems is a major challenge for the coming years in order to protect and preserve the quality and quantity of river water. In addition, dam or power plant managers are obliged by European and national rules to renaturalize parts of their operating area. Unfortunately, restoration experiences are few and far between. A synthesis of feedbacks has been undertaken for restoration operations along the Rhine (https: // obs-rhin.engees.eu/). Operation reports and interview sheets have also been collected but not used. However, this information, including feedback on errors or unexpected results, would be essential to guide or help implement new projects.
3.Activities
· Description of the research activities:
To exploit this information, we propose to develop AI methods relying on case- based reasoning (CBR). CBR consists of solving new problems by reusing the solution of similar problems that have already been solved. A case corresponds to a problem-solving episode usually represented by a problem-solution pair. Cases are recorded in a case base. A source case is an element of the case base. The CBR process consists of solving a new problem, the target problem, using the case base. A common way of doing this is to select a source case similar to the target problem (case retrieval step) and to modify the retrieved case so that it provides a solution that hypothetically solves the target problem (case adaptation step). After these inference steps, some learning steps are sometimes implemented. CBR has been used on many applications e.g., agriculture or cooking. The following steps are to be developed:
Step 1: Structuration of a case base: information extracted from existing websites and texts will be formalized within cases (a renaturation operation, context, documents and results, etc.). Text mining will be performed by partner Thalès to extract information from textual documents. A graph representation will be researched to represent the information for each operation (knowledge graphs / conceptual graphs). Different levels of representation will be studied and organized in a hierarchical way, with regards to domain knowledge. Expert rules can also be used to complete the cases. Cases will be provided to to populate the ontology developed in another work package (WP4).
Step 2: Problem description and definition of the case retrieval step: this step will rely on graph matching, including approximative graph matching based on ontological reasoning, in relation with WP4. Various similarity measures and graph mining approaches will also be experimented.
Step 3: Solution adaptation: an adaptation approach based on the semantical and numerical information describing both source and target cases will be studied. Besides, A query interface will be developed with the help of an engineer (6 moths) or the decision support system: the user describes a site or an operation and the system retrieves and displays similar sites/operations with their results., and suggests adaptions.
Références
O. Bruneau, E. Gaillard, N. Lasolle, J. Lieber, E. Nauer, J. Reynaud, A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning. In: ICCBR 2017. pp 76–91. Trondheim, Norway, 2017.
V. Dufour-Lussier, F. Le Ber, J. Lieber, L. Martin, Case Adaptation with Qualitative Algebras, in Twenty-Third IJCAI, Beijing, China, 2013.
A. Inokuchi, T. Washio, H. Motoda, An Apriori-Based Algorithm for Mining Frequent Sub-structures from Graph Data, in 4th European Conference, PKDD, pp. 13–23. Lyon, France, 2000.
F. Le Ber, A. Milles, L. Martin, X. Dolques, M. Benoît, A Reasoning Model based on Perennial Crop Allocation Cases and Rules, in ICCBR 2017, pp 61–75. Trondheim, Norway, 2017.
F. Le Ber, A. Napoli, J.-L. Metzger, S. Lardon, Modeling and Comparing Farm Maps using Graphs and Case-based Reasoning, Journal of Universal Computer Science, 2003, 9 (9), pp.1073-1095
G. Li, L. Yan, Z. Ma. An approach for approximate subgraph matching in fuzzy RDF graph, Fuzzy Sets and Systems, 2019, 36, pp. 106-126.
F. Liu, Y. Wang, Z. Li, R. Ren, H. Guan, X. Yu et al. MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting. In ICCBR 2022, pp. 224–239. Nancy, France, 2022.
C. K. Riesbeck und R. C. Schank, Inside Case-Based Reasoning, Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1989.
· Related activities :
Meetings and collaboration with other partners of the projet (especially Thalès), collaboration with domain experts (hydroecology)
4. Skills
· Qualifications/knowledge:
PhD in computer science
· Operational skills/expertise:
Knowledge modeling, ontology-based reasoning, knowledge graphs, graph algorithms.
Languages: Python, java, owl/sparql.
Knowledge in qualitative spatial modeling, and GIS would be appreciated.
· Personal qualities:
Interest in the application domain, ability to work with experts who are not computer scientists. Ability to work collaboratively.
5. Environment and context of work
· Presentation of the laboratory/unity:
Created in 2013, ICube laboratory brings together researchers of the University of Strasbourg, the CNRS (French National Center for Scientific Research), the ENGEES and the INSA of Strasbourg in the fields of engineering science and computer science, with imaging as the unifying theme. With around 650 members, ICube is a major driving force for research in Strasbourg whose main areas of application are biomedical engineering and the sustainable development.
The "Data Science and Knowledge" team covers a large spectrum of research in computer science, more precisely in artificial intelligence. Our research activities focus on two theoretical research themes: Machine learning; Data and knowledge. We are specialist of some data types and we have a few privileged application domains, among which is the environmental (water, geography, agriculture) domain.
See https: // sdc.icube.unistra.fr/en/index.php/Home
· Hierarchical relationship:
The recruited person will work under the responsibility of Florence Le Ber who is the responsible for ICube partner in the TETRA project.
· Special conditions of practice (notice attached):
Place: UMR ICube, Strasbourg and Illkirch, France – Project meetings will take place mainly in Karlsruhe (Germany) and Saclay (Paris region).
Salary: 2100-2700歐元 net/month (according to the experience)
To apply, please send your CV, cover letter and diploma to:florence.leber@engees.unistra.fr
Requirements
Research Field
Computer science
Education Level
PhD or equivalent
Internal Application form(s) needed
RecrutementPostDoctorantsTetraG.pdf
English
(74.04 KB - PDF)
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Additional InformationWork Location(s)
Number of offers available
1
Company/Institute
Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie – ICUBE, UMR 7357
Country
France
City
Illkirch-Graffenstaden
Geofield
Where to apply
florence.leber@engees.unistra.fr
Contact
City
Strasbourg
Website
https:// www. unistra.fr
Street
4 rue Blaise Pascal
Postal Code
67000
STATUS: EXPIRED
