Doktorand Graph Machine Learning & Financial Crime Analytics (w|m|d) | findic
Core
Develop machine learning methods to detect suspicious transaction patterns and complex anomalies in transaction and relationship networks for financial crime detection.
Role type
Research PhD student (Graph Machine Learning)
Builds
Innovative AI solutions for banks and insurance companies
Domain
Financial services, Graph Intelligence, Machine Learning
Deliverable
research
Required skills
Graph Neural Networks, self-supervised learning, contrastive pre-training, graph construction, entity resolution, Python, PyTorch, Git, model evaluation
Preferred skills
Neo4j, Knowledge Graphs, Property Graphs, graph-based anomaly detection, Fraud Detection, AML, Docker, model deployment
Technologies
PyTorch, PyTorch Geometric, DGL, GCN, GAT, GraphSAGE
Responsibilities
Develop ML procedures for detecting suspicious transaction patterns and complex anomalies in networks; investigate modern approaches like GNNs and contrastive pre-training; evaluate new methods regarding performance, robustness, and explainability; translate suitable approaches into innovative AI solutions.
Seniority
PhD Candidate / Researcher
