Job Description

The candidate will work closely with the research team to develop advanced methods in Reinforcement Learning under Uncertainties for Reliable Autonomous Decision-Making in Finance. 

Primary responsibilities will include:
•    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 decision-making in finance, 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 optimal strategies.
•    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 and equity markets, to contribute valuable insights to research projects.
•    Communicate research outcomes through peer-reviewed publications and other scholarly dissemination channels.

Qualifications / Discipline:

•    PhD in Mathematics/Statistics/CS. 

Skills:
•    Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning.
•    Strong programming skill  in Python, TypeScript, and JavaScript, along with frameworks like PyTorch. 
•    Excellent problem-solving abilities and attention to detail. Strong written and verbal communication skills.

Experience:
•    Strong preference for candidates with peer-reviewed publications, patents, conference presentations, research grants, or industry R&D contributions.