- Machine Learning
Post-Doctoral Research Fellow
The Department is seeking a Post-doctoral Research Fellow to conduct high-quality research in harmonic and time series analysis, signal processing and machine learning. The Project is related to the application of machine learning in financial modeling.
Applicants should have/be:
- a PhD degree in Applied Mathematics, Physics, Electrical Engineering, Computer Science, Statistics or related disciplines;
- knowledge of linear algebra and harmonic analysis, at the level of an undergraduate degree in Mathematics or Physics;
- skills and passion to programme in C/C++, Python, or R on Windows or Linux platforms;
- adaptive to learning new programming languages;
- experience in application packages for machine learning (or deep learning is a definite advantage); and
- good communication in both spoken and written English and Chinese.
Initial appointment will be made on a fixed-term contract of one year. Re-appointment thereafter is subject to mutual agreement and availability of funding.
Salary will be commensurate with qualifications and experience.
Applicants are invited to write in response to the requirements and fill in the application form which is obtainable (a) by downloading from http://pers.hkbu.edu.hk/applicationforms; or (b) by fax at 3411-7799; or (c) in person from the Personnel Office, Hong Kong Baptist University, AAB903, Level 9, Academic and Administration Building, 15 Baptist University Road, Kowloon Tong, Kowloon. Completed application form should be sent to the same address. Please quote PR number on all correspondence. Applicants not invited for interview 8 weeks after the closing date may consider their applications unsuccessful. Details of the University’s Personal Information Collection Statement can be found at http://pers.hkbu.edu.hk/pics.
The University reserves the right not to make an appointment for the post advertised, and the appointment will be made according to the terms and conditions then applicable at the time of offer.
Closing date: 28 February 2017