Research Scientist in Semantic Web: Federated Data Interoperability and Query Optimization
Publication date:
26 October 2024Workload:
100%Contract type:
Permanent position- Place of work:Lausanne
The SIB Swiss Institute of Bioinformatics is an internationally recognized non-profit organization, dedicated to biological and biomedical data science. Its data scientists are passionate about creating knowledge and solving complex questions in many fields, from biodiversity and evolution to medicine. They provide essential databases and software platforms as well as bioinformatics expertise and services to academic, clinical, and industry groups. SIB federates the Swiss bioinformatics community of some 900 scientists, encouraging collaboration and knowledge sharing. The Institute contributes to keeping Switzerland at the forefront of innovation by fostering progress in biological research and enhancing health.
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To reinforce our team in Lausanne, Switzerland, we are seeking a
Research Scientist in Semantic Web: Federated Data Interoperability and Query Optimization
- R&D to build a federated query engine that encapsulates interoperable knowledge graphs. This engine will process pre-defined mappings and links across data sources at query execution time. In addition, ideally, the engine will optimise the federated query execution plan.
- R&D to identify and model key metadata for federated data interoperability and query optimization over real-world knowledge graphs.
- R&D on innovative algorithms for semantic data interoperability (e.g., finding links and mappings among knowledge graphs).
- To investigate machine learning approaches, and possibly, fine-tune existing large language models for the tasks of semantic data interoperability and query optimisation.
- Provide a single data access point that masks the complexities of effectively making the knowledge graphs interoperable.
- To contribute and directly collaborate with other researchers in improving our LLM-based system that generates SPARQL queries from plain text.
- Publish and present the resulting work as peer-reviewed articles at conferences and scientific journals.
- Ph.D. in Computer Science (or in a related field) with maximum 5 years of relevant experience.
- Experience with federated data interoperability and query processing.
- Familiarity with devising and incorporating both Machine Learning and non-Machine learning algorithms in query optimization.
- Semantic Web expertise (e.g., RDF, OWL, SPARQL, SHACL, ShEx).
- Software development (e.g., Rust, C++, Python, Java, version control with Git).
- Proven ability to carry out independent research and software development.
- Excellent oral and written communication skills.
- Track record of publications in top-tier conferences and journals.
- Experience with life science datasets is a plus.
- Openness to working and collaborate in a highly interdisciplinary environment.
- Proficiency in English is required. French or German is a plus but not mandatory.