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Research Scientist in LLMs for scientific data exploration

SIB Institut Suisse de Bioinformatique
  • Veröffentlicht:

    26 Oktober 2024
  • Pensum:

    100%
  • Vertragsart:

    Festanstellung
  • Arbeitsort:Zurich

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.

Curious? Please click here to learn more about working at SIB.



To reinforce our team in Zürich, Switzerland, we are seeking a


Research Scientist in LLMs for scientific data exploration


Job description

Reporting to a Team Lead at the Knowledge Representation Unit, the successful candidate will play an important role in shaping key scientific projects within this Unit, as part of a team of researchers and software engineers. Additionally, the applicant will contribute to publications in top-tier journals and conferences in the field, as well as defining research directions for the future of the Unit.

The main activities will be: 
  • To build upon state-of-the-art Large Language Models for developing innovative question answering systems over scientific data sources available as Knowledge Graphs. 
  • To leverage LLMs for Knowledge Graph construction (e.g., triple extraction from unstructured data).
  • To contribute to the implementation of a system for generating SPARQL queries from scientific questions. 
  • To work on data harmonisation, create data schemas and perform data modelling using Semantic Web Technologies when appropriate.  
  • To build and maintain knowledge graphs based on the created or adopted data schema.  
  • To be able to negotiate and coordinate domain experts in life sciences to find an agreement for accurately defining domain-specific semantic models.  
  • 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

  • PhD degree in computer science or in a related field with maximum 5 years of relevant experience. 
  • Hands-on experience using and fine-tuning Large Language Models. 
  • Experience in Applied Machine Learning and LLMs to generate structured queries (e.g., SPARQL) would be a significant plus.
  • Semantic Web Technologies’ expertise (e.g., RDF, RDFS, OWL, SPARQL, SHACL).
  • Familiarity with ontology engineering best practices and natural language processing would be a plus. 
  • Software development (e.g., Python, Rust, Java, version control with Git).
  • Experience with life science datasets (including biomedical data) is a plus.  
  • 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.
  • Openness to working in a highly interdisciplinary and dynamic environment.
  • Multi-tasking, openness to working on multiple projects with similar responsibilities. 
  • Proficiency in English is required. Speaking 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 LLMs for scientific data exploration, please submit your application including CV and letter of motivation through our online portal by clicking the "Apply" button.