Research Scientist in Semantic Web: Federated Data Interoperability and Query Optimization

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 Job description
Reporting to a Team Lead at the Knowledge Representation Unit, the candidate will actively contribute to the rapid development of the unit, ensuring a strong synergy between scientific expertise and innovation. The candidate will be part of a team composed of researchers in computer science and software engineers. More precisely, the applicant will contribute to improving the ability to answer complex, high-level scientific questions exploiting decentralized data sources by leveraging mechanisms to query distributed knowledge graphs. The candidate will focus on cutting-edge research that lies at the intersection of applied machine learning, semantic web, federated data interoperability and query optimization.

The main activities will be:

• 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.

The position is temporary for 3 years, with the possibility of extension.


Profile requirements

• 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.

How to apply
SIB is committed to ensuring and fostering diversity and equal opportunities in the workplace as well as in the scientific ecosystem. We encourage candidates to apply even if they do not match all profile requirements. If you are highly motivated to advance the area of federated data interoperability and query optimisation, please submit your application including CV and letter of motivation through our online portal by clicking the "Apply" button.