Knowledge Graphs:
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Experience with building and querying knowledge graphs.
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Proficiency in languages and tools such as (Geo)SPARQL, RDF and OWL
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Understanding of ontologies and semantic web technologies.
Machine Learning:
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Strong foundation in machine learning algorithms and methodologies.
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Experience with popular ML frameworks such as TensorFlow and PyTorch
Ability to- ML techniques to graph data.
Graph Neural Networks (GNN):
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Hands-on experience with GNNs, including familiarity with libraries like PyTorch Geometric or DGL.
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Understanding of different GNN architectures and their applications.
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Experience in implementing and optimizing GNN models for various tasks.
Data Handling and Preprocessing:
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Skills in data cleaning, transformation, and preparation, especially for graph data.
Programming Languages:
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Proficiency in Python, with experience in libraries and tools for data manipulation and analysis (e.g., Pandas, NumPy).
