Tasks
Research tasks:
Literature research on current GNN architectures and their fields of application
Comparison of existing methods for link prediction in knowledge graphs
Analysis of benchmarks and evaluation metrics for link prediction
Research of best practices for using ontologies in IT asset management
Requirements
Degree programs:
Computer Science
Business Information Systems
Data Science / Artificial Intelligence
Natural Sciences with a focus on Data Science
Specializations:
Machine Learning / Deep Learning
Network Analysis or Graph Theory
Databases
Artificial Intelligence
Software Architecture
Expert knowledge:
Basic understanding of knowledge graphs and ontologies and/or a high level of willingness to learn them
Knowledge of machine learning / deep learning
Understanding of graph neural networks (GCN, GAT, R-GCN) and willingness to delve deeper into them
Fundamentals of evaluation methods (accuracy, precision, recall, F1-score)
Knowledge of ontology optimization and data quality would be a plus
IT skills:
Python
Experience with PyTorch or Tensorflow/Keras
Experience with Networkx or comparable graph libraries
Handling Excel/CSV-based data sources
(Optional) Knowledge of cloud systems, preferably Azure
Soft skills:
Analytical thinking and high self-motivation to delve into complex topics
Ability to work with scientific literature
Independent problem-solving skills, but also the ability to work in a team with supervisors
Profound scientific writing skills
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