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[Job] Research Associate/PhD Candidate “Computer Science: Natural Language Processing & Semantic Web Technologies” (m/f/d), TIB – Leibniz Information Centre for Science and Technology, Germany
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May 24, 2022, 7:37:40 AM5/24/22
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University or Organization: TIB – Leibniz Information Centre for Science and Technology and University Library
Department: Data Science and Digital Libraries
Job Location: Hannover, Germany
Web Address: https://www.tib.eu/en/research-development/research-groups-and-labs/data-science-digital-libraries
Job Title: Research Associate/PhD Candidate
Job Rank: PhD Candidate
Specialty Areas: Natural Language Processing; Computational Linguistics; Software Engineering; Computer Science
Description:
The PhD topics will be in the context of the Open Research Knowledge Graph (https://www.orkg.org) and the project “SCINEXT - Neural-Symbolic Scholarly Innovation Extraction”, funded by the Federal Ministry of Education and Research (BMBF). The aim of these projects is to research and develop techniques for crowdsourcing, representing and managing semantically structured, rich representations of scholarly contributions and research data in knowledge graphs and thus develop a novel model for scholarly communication. In the context of the PhD thesis you will be responsible for building and maintaining the ORKG data ingestion and processing pipelines to ensure the flow of high-quality semantified resources from publications. Your main responsibility in this position will be to build scalable solutions that crawl, ingest, process publications, and thereby enrich the ORKG. You will work alongside the ORKG engineering team to set up the AI/NLP ecosphere.
Your tasks will focus on
Working in the areas of Natural Language Processing (text mining, information extraction, information retrieval/search) and Machine Learning of scholarly communication media (digital) data.
Identifying and implementing the tools and algorithms appropriate for Natural Language Processing assignments to enhance the NLP system currently in place.
Conceptually designing, modeling, and implementing data-driven services for information retrieval and extraction, data enrichment, and linking of data.
Carrying out evaluation experiments and training the developed model.
Application Deadline: Open as long as filled
Web Address for Applications: https://www.tib.eu/en/tib/careers-and-apprenticeships/vacancies/details/stellenausschreibung-nr-31-2022
Contact Information:
Dr. Jennifer D'Souza
Email: jennife...@tib.eu
Department: Data Science and Digital Libraries
Job Location: Hannover, Germany
Web Address: https://www.tib.eu/en/research-development/research-groups-and-labs/data-science-digital-libraries
Job Title: Research Associate/PhD Candidate
Job Rank: PhD Candidate
Specialty Areas: Natural Language Processing; Computational Linguistics; Software Engineering; Computer Science
Description:
The PhD topics will be in the context of the Open Research Knowledge Graph (https://www.orkg.org) and the project “SCINEXT - Neural-Symbolic Scholarly Innovation Extraction”, funded by the Federal Ministry of Education and Research (BMBF). The aim of these projects is to research and develop techniques for crowdsourcing, representing and managing semantically structured, rich representations of scholarly contributions and research data in knowledge graphs and thus develop a novel model for scholarly communication. In the context of the PhD thesis you will be responsible for building and maintaining the ORKG data ingestion and processing pipelines to ensure the flow of high-quality semantified resources from publications. Your main responsibility in this position will be to build scalable solutions that crawl, ingest, process publications, and thereby enrich the ORKG. You will work alongside the ORKG engineering team to set up the AI/NLP ecosphere.
Your tasks will focus on
Working in the areas of Natural Language Processing (text mining, information extraction, information retrieval/search) and Machine Learning of scholarly communication media (digital) data.
Identifying and implementing the tools and algorithms appropriate for Natural Language Processing assignments to enhance the NLP system currently in place.
Conceptually designing, modeling, and implementing data-driven services for information retrieval and extraction, data enrichment, and linking of data.
Carrying out evaluation experiments and training the developed model.
Application Deadline: Open as long as filled
Web Address for Applications: https://www.tib.eu/en/tib/careers-and-apprenticeships/vacancies/details/stellenausschreibung-nr-31-2022
Contact Information:
Dr. Jennifer D'Souza
Email: jennife...@tib.eu
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