ORGANISATION/COMPANYSofia University St. Kliment Ohridski - GATE institute - Big Data for Smart Society
RESEARCH FIELDComputer scienceEngineeringInformation scienceMathematics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE26/03/2021 23:00 - Europe/Brussels
LOCATIONBulgaria › Sofia
TYPE OF CONTRACTTemporary
EU RESEARCH FRAMEWORK PROGRAMMEH2020
Nowadays Artificial Intelligence (AI) increasingly requires a strong integration of symbolic Knowledge Representation and Reasoning (e.g., ontologies, knowledge graphs, semantic web) and Machine Learning approaches. The key reason for this is that ML-based approaches expect or can be significantly improved by training them with high-quality and high-dimensional data. Building high-dimensional data, i.e., data that describe a certain phenomenon with a rich set of variables (rich data) is a difficult and effort-consuming task, especially when the quality needs to be preserved. It has estimated that up to 80% of the effort in data analytics projects is spent on preparing the data for the analytical modelling phase. Rich data is usually the result of the integration of data coming from different sources tackling semantic interoperability problems, such as the usage of different formats (e.g., CSVs vs. RDBs), different systems of identifiers (e.g., Place names vs. place identifiers), and different schemas (e.g., different attributes used to model the same domain).
Knowledge graphs have become the preferred abstraction to support data integration and solving semantic interoperability challenges, not only in the academy, but also in the public government domain (Linked Open Data initiatives) and in the industry (Google Knowledge Graph). Building knowledge graphs and enriching data using explicitly represented knowledge in symbolic form require addresses several research areas like data management, ontology engineering, semantic modelling, knowledge representation and logics.
Despite a large number of technologies developed, a significant lack of efficient and effective solutions to the problems of data enrichment and semantic interoperability still exists, which prevents the development of AI applications on top of rich data.
We are looking for a mixture between a scientist, engineer and philosopher who can create and maintain complex Knowledge graphs that amalgamate multiple data sources to represent complex entities in the GATE application domains: Future Cities, Digital Health, Smart Industry and Intelligent Government. Our projects take many forms and require a diverse and flexible skillset that let you solve complex data integration and analytical issues quickly and proficiently.
Junior researcher(R1)- Ontologies and Knowledge graphs
• Design, implement and maintain complex Linked Data models;
• Uncover data and their relationships from a variety of sources: relational databases, flat files and RDF documents, other;
• Provide efficient data integration in GATE application domains;
• Create and instantiate OWL ontologies to expose semantics encoded in the data;
• Construct and maintain ETL pipelines to keep Linked Data resources up to date from various sources;
• Scientific paper writing;
• Keep up-to-date with latest technology trends;
• Engage in appropriate training and development opportunities.
You will join and collaborate within a team of researchers, but will have the freedom to conduct research in any area within the scope and priorities of GATE, creating new visions for the future;
You will be provided with numerous opportunities for learning, knowledge exchange and career development, locally and internationally;
Your research will be supported by an advanced research infrastructure, comprising of the GATE platform and Open Innovation Labs;
You will have a flexible work schedule and, upon the move to the new premises of GATE – modern and appealing work environment, stirring up creativity and productivity;
You will be provided with competitive working conditions and salary commensurate with your skills and experience.
To apply for these positions, please send to email@example.com:
A CV detailing previous employments and qualifications.
A Personal letter of 1-2 pages where you:
Describe your past participation in research and main research results
Describe your future goals and future research focus.
Copies of diplomas for completed education.
Copies of other diplomas and certificates of qualification.
Copies of documents certifying past work experience in the relevant field.
Interviews with the selected applicants will be held online.
By applying for these positions, you voluntarily provide your personal data and consent to be processed for the purpose of recruitment and selection of personnel. The processing of your personal data shall be carried out in accordance with the requirements of Regulation (EU) 2016/679 (General Data Protection Regulation), the Personal Data Protection Law and related legal acts in Bulgaria.
REQUIRED LANGUAGESENGLISH: Excellent
Skills, Experience and Qualifications
• А minimum of bachelor's or master's degree in computer science, geoinformatics/geomatics, applied mathematics, engineering, or in a related discipline;
• Expertise with two or more of the following technologies Graph and Semantic technologies such as Neo4J, Graph DB / Triple Stores like Stardog, NoSQL DBs, SPARQL, Cypher, Gremlin, Protégé, RDF, OWL, R2RML;
• Familiarity with graph Analytics & Inferencing / Visualization tools such as Cytoscape, Cytoscape js, Gephi, GraphViz, Neovis.js and especially related to GIS: Leaflet.js, Folium and similar;
• Knowledge of and experience in implementing Object Oriented design and experience in at least one programming language like Java, Scala, C++, C# or similar and at least one scripting language such as Python or R;
• Solid oral/written communication and presentation skills in order to explain the outcome of research (and value thereof) to coworkers and peers;
• Ability to prioritize tasks and work on multiple assignments, and a deep desire to find ways to create value and deliver results.
• Fluent English is a must.
Experience with one or more of the following
• Experience or familiarity with AI and Machine learning concepts: Supervised and Unsupervised machine learning, Neural Networks, Support vector machines, Kernel methods;
• Experience in working with Jupyter notebook/Jupyter lab and other tools for reproducible research;
• Experience in domains where knowledge graphs can be applied like GIS, Bioinformatics and Health.
EURAXESS offer ID: 609827
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