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Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.
NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Mathematics%29/33227-en_GB/
We regret that only shortlisted candidates will be notified.
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.
. 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.
Job ID: 148868589
Skills:
stochastic control , Dynamic Programming, Python, probability theory, reinforcement learning environments, reinforcement learning, machine learning libraries, optimal transport, distributionally robust optimization, Optimization, Markov decision processes, numerical experiments, stochastic processes
Skills:
dynamical systems, PDE theory, mathematical machine learning
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