Postdoctoral Fellow in Financial Technology
7 days left
- Full Time
The University of Hong Kong 's highest priorities are to create opportunities for the very best academic talents to excel and to advance human knowledge to the benefit of society. We serve the needs of Hong Kong, the wider region and the rest of the world.
Work type: Full-time
Department: Faculty of Business and Economics (07030)
Categories: Academic-related Staff
Applications are invited for appointment as Post-doctoral Fellow in Big Data and Financial Technology (FinTech) in the Faculty of Business and Economics (Ref.: 500070), to commence as soon as possible for two years.
Applicants should possess a Ph.D. degree in Finance, Computer Science, Mathematics or a relevant field. They should demonstrate excellent ability to communicate effectively both in writing and in oral presentations in Chinese and English. Preference will be given to those with prior experience in or knowledge of Financial Technology (FinTech).
The appointee will work closely with the Principal Investigator to conduct research under his supervision in a predominantly independent manner, and pursue investigations on applying machine learning and modern Artificial Intelligence techniques in the field of FinTech. The related topic includes but not limited to risk management, alternative data processing, financial asset pricing, robot advisory, quantitative trading, smart contract and blockchain.
A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.
The University only accepts online application for the above post. Applicants should apply online and upload an up-to-date C.V, research papers, three confidential reference letters, and other supporting documents. Potential applicants are welcome to contact Professor Chen Lin (e-mail: firstname.lastname@example.org). Review of applications will start as soon as possible and continue until July 15, 2020, or until the post is filled, whichever is earlier.