Formulate and drive original research directions in graph learning for fraud detection, exploring architectures such as GNNs, graph transformers, and hybrid models
Design scalable approaches for dynamic, heterogeneous, and large-scale fraud graphs
Investigate agentic AI and LLM-augmented systems for automated risk reasoning, investigation workflows, and decision support
Develop robust learning techniques for adversarial and non-stationary environments
Conduct rigorous empirical evaluation on real-world, production-scale datasets
Translate research insights into practical system-level implications in collaboration with data scientists and engineers
Contribute to patent development and preparation of submissions to top-tier academic venues

Requirements

Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
Strong research foundation in one or more of:
- Graph representation learning (e.g., GNNs, graph transformers)
- Transformer architectures and deep learning
- LLMs and agentic AI systems
- Adversarial, robust, or trustworthy machine learning
Demonstrated research capability (e.g., publications, preprints, or equivalent work)
Strong programming skills in Python
Experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)
Ability to independently scope and execute open-ended research problems

Benefits

Offers Equity
Offers Bonus

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

graph learningGNNsgraph transformershybrid modelsadversarial machine learningrobust machine learningtrustworthy machine learningPythondeep learningLLMs

Soft Skills

research capabilityindependent problem solvingcollaboration

Certifications

PhD in Computer SciencePhD in Machine LearningPhD in StatisticsPhD in Mathematics