Neuro-symbolic approaches for energy knowledge graphs interoperability
Investigate neuro-symbolic methods combining data semantics and machine learning to improve interoperability in energy knowledge graphs.
Best for:Best for computer science graduates with a strong foundation in machine learning, knowledge representation, and semantic web technologies.
Overview
This PhD project is hosted by Université Paris-Saclay GS Informatique et sciences du numérique, focusing on the integration of renewable energy systems and the digital transformation of the energy sector. The research aims to overcome data silos and semantic interoperability hurdles across electricity, gas, and heat networks. By combining data semantics, reasoning rules, and machine learning knowledge graph embeddings, the candidate will develop advanced neuro-symbolic approaches. The expected output is an enhanced learning model capable of predicting entity relationships to support future smart energy infrastructures.
What you'll do
- Investigate novel neuro-symbolic approaches merging data semantics with machine learning knowledge graph embeddings
- Address semantic interoperability challenges across heterogeneous energy data models and formats
- Design and implement reasoning rules and inference mechanisms to enhance machine learning prediction models
- Analyze complex multi-vector energy data sources including electricity, gas, and heat networks
- Contribute to scientific publications and present research findings at academic conferences
Requirements
Degree required: Master's degree or equivalent in Computer Science or a related field
Degree offered: PhD
Language: English or French proficiency as required by the host institution
Instruction: English / French
- Solid background in computer science fundamentals
- Experience with machine learning methodologies
- Knowledge representation and reasoning experience
- Understanding of semantic web technologies and ontologies
- Hold a recognized Master's degree or equivalent prior to the start date
Funding & terms
How to apply
Deadline: 2026-10-31
Prepare your academic transcripts, a detailed curriculum vitae covering main projects and your master thesis, along with recommendation letters. Submit the complete documentation package before the specified deadline following the portal instructions.
- CV
- Transcripts
- Recommendation letters
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