Knowledge Graph Engineer (W/M)

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 16,000 people, including over 12,000 students and 4,000 researchers from more than 120 different countries.

The aim of the EPFL Blue Brain Project (BBP), a Swiss brain research initiative founded and directed by Professor Henry Markram, is to establish simulation neuroscience as a complementary approach alongside experimental, theoretical and clinical neuroscience to understanding the brain, by building the world’s first biologically detailed digital reconstructions and simulations of the mouse brain.

Blue Brain’s Data and Knowledge Engineering team is building a Knowledge Graph used to perform large-scale semantic and spatial data integration, exploration, inference and access within 3D Brain Atlases to support biologically detailed digital reconstructions and simulations of the mouse brain. Graph analytics as well as state of the art methods for machine learning on graphs (e.g. Graph Embeddings, Graph Neural Networks, Graph-to-Text generation) are applied on top of the knowledge graph to support scientific downstream tasks through for example recommender and inference systems, link prediction, NLP language model augmentation with knowledge background.
To strengthen its Data and Knowledge Engineering team, BBP is looking for a: Knowledge Graph Engineer (W/M).

• In relation with data providers and consumers, develop data and knowledge structures as well as state of the art methods for machine learning on graphs (e.g. Graph Embeddings, Graph Neural Networks, Graph-to-Text generation) to support scientific downstream tasks through (but not limited to) recommender and inference systems, link prediction, NLP language model augmentation with knowledge background.

• Develop pipelines to semantically represent in a knowledge graph unstructured and structured data from different sources and of various formats to support reasoning with knowledge graphs

• As a team player, participate in the design and implementation of features in the BBP knowledge graph technology stack

• Implement best-practices for maintainable data-driven products and deliver production quality code following best-practices for maintainable software development

• Hands-on experience with Python
• Hands-on experience in graph analytics and methods for machine learning on graphs.
• Hands on experience with at least one machine learning library (TensorFlow, PyTorch)
• Experience with Graph Databases (Property graphs such as Neo4J, Triple stores)

• Experience in Semantic web technologies and formats (RDF, SPARQL, SHACL)
• Experience in querying data using ElasticSearch,
• Experience building containerized applications with Docker

• Master degree in Computer Science, Physics, Applied Maths or similar fields
• 2+ years of relevant experience in information retrieval, machine learning on graphs, or related domains
• English written and spoken. French would be a plus.

• A world-recognized leader in simulation-based research in neuroscience
• A dynamic, multidisciplinary, international and collaborative working environment committed to benefitting the global community
• A modern working environment, based at the Biotech Campus in Geneva Sécheron

Start date :
as soon as possible

Term of employment :
Fixed-term (CDD)

Work rate :
100%

Duration :
1 year, renewable

Remark :
Only candidates who applied through EPFL website or our partner Jobup’s website will be considered. Files sent by agencies without a mandate will not be taken into account.

Reference :
Job Nb 2870