"Postdoc in Scalable Graph Learning"

Job Description

Summary: This position involves research in Graph Machine Learning (Graph ML), focusing on Graph Neural Networks (GNNs) for applications in financial domains such as detecting money laundering and financial fraud. The project explores integrating GNNs with Large Language Models (LLMs) and addresses challenges like scalability, efficiency, privacy, and resilience against adversarial attacks.

Responsibilities:

• Develop machine learning solutions using GNNs and LLMs for graph-structured data in financial transaction networks.
• Investigate methods to handle scalability, efficiency, privacy, and adversarial robustness in Graph ML applications.

Qualifications and Requirements:

• A PhD degree in Computer Science, Mathematics, Electrical Engineering, or a related discipline, with a thesis in machine learning, deep learning, or parallel and distributed computing.
• Experience in graph machine learning, federated learning, differential privacy, or adversarial robustness.
• Hands-on experience with Deep Neural Networks using PyTorch or TensorFlow, and preferably with GNNs using PyTorch Geometric or Deep Graph Library.
• First-author publications at leading conferences in artificial intelligence, machine learning, security and privacy, or data management.

What the Employer Offers:

• An 18-month contract for 36-40 hours per week.
• Salary and benefits in accordance with the Collective Labour Agreement for Dutch Universities.
• Customizable compensation package, discounts on health insurance, monthly work costs contribution, and flexible work schedules.
• Support for relocation, including events to help settle in the Netherlands and a Dual Career Programme for accompanying partners.