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About the Asian Institute of Digital Finance (AIDF)
The Asian Institute of Digital Finance (AIDF) is a university-level institute in the National University of Singapore (NUS), jointly founded by The Monetary Authority of Singapore (MAS), the National Research Foundation (NRF) and NUS. AIDF aspires to be a thought leader, a FinTech knowledge hub, and an experimental site for developing digital financial technologies as well as for nurturing current and future FinTech researchers and practitioners in Asia.
Duties and Responsibilities
The Research Assistant will be responsible for working closely with the Principal Investigator on quantitative finance and fintech research projects. In particular, the research assistant is going to develop Machine Learning/Reinforcement Learning methods and apply them in the field of quantitative finance and risk management.
- Data Management: Utilize proficiency in SQL, NoSQL, and vector databases to collect, clean, and manage data for research projects.
- Research Assistance: Collaborate on research initiatives focused on credit recovery efficiency, applying quantitative finance and econometric methodologies to analyze data and develop models.
- Machine Learning: Apply expertise in reinforcement learning and traditional machine learning algorithms to develop predictive models and optimization strategies for credit recovery processes.
- Software Development: Leverage software development experience to create and maintain tools and applications necessary for research and data analysis.
- Cloud Infrastructure: Manage and optimize cloud infrastructure in AWS to support research initiatives and ensure scalability.
- Programming: Utilize skills in Python, TypeScript, and JavaScript, along with frameworks like PyTorch, to implement and test quantitative models.
- Financial Expertise: Apply knowledge of finance, especially in risk management, to contribute valuable insights to research projects.
Qualifications
- The candidate should have a Bachelor degree in Mathematics/Quantitative Finance or a related field. The candidate should have strong familiarities with quantitative finance problems such as portfolio optimization, credit rating, market making, and derivative pricing.
- The candidate should have expertise and research experiences in the areas of machine learning/reinforcement learning on quantitative finance problems. The candidate should have deep understandings of Markov Decision Process, Model-based and Model-free RL, Value/Policy Iterations, Q-learning, as well as designing and training of Neural Networks.
- Candidates who have experience with model uncertainty, model robustness, and inverse learning will be highly preferred.
- The candidate should have shown academic excellence and promising research potential. Strong publication record in internationally well-recognized journals is highly preferred. International scholarly visibility (e.g. conference presentations, academic visits) would also be favoured.
- The candidate should be an independent researcher as well as a good team player. The potential research projects might involve collaboration from various disciplines. Strong interpersonal, leadership, and project management skills are highly preferred.
