PhD Research Intern – Graph Learning & Agentic AI for Fraud Detection

📋 Description

• Research graph learning for fraud detection (GNNs/graph transformers)
• Design scalable approaches for dynamic, large-scale fraud graphs
• Investigate agentic AI and LLM-augmented systems for risk reasoning
• Develop robust learning techniques for adversarial and non-stationary environments
• Evaluate models on real-world, production-scale datasets
• Translate findings into practical system-level insights with data scientists and engineers
• Contribute to patent development and submissions to top-tier venues

🎯 Requirements

• Currently pursuing a PhD in CS, ML, Stats, Math, or related field
• Graph representation learning (GNNs, graph transformers)
• Transformer architectures and deep learning
• LLMs and agentic AI systems
• Adversarial, robust, or trustworthy ML
• Strong programming skills in Python
• Experience with PyTorch, TensorFlow, JAX
• Ability to independently scope and execute open-ended research problems