Research Fellow (Machine Learning for Time-Series Analysis)

Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore

The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and photonics. Over the years, SPMS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers.

Position Available: Research Fellow

Join the laboratory of Tong Ping at the School of Physical and Mathematical Sciences. We are seeking a research fellow to contribute to a research project on AI for seismic safety and sustainability.

Key Responsibilities:

• Develop novel machine learning theories and techniques for analyzing noisy time-series data, with a particular focus on seismic signals

• Perform uncertainty quantification in time-series analysis to assess model reliability and predictive confidence

• Publish high-quality scientific results in Tier-1 journals

Job Requirements:

• A PhD degree in Mathematics, Computer Sciences, or related areas, with focus on machine learning

• Experience in time-series data analysis

• Proficient in coding languages, such as C, C++, Fortran, and Python

• Strong publication record

• Entry level candidates are welcome to apply

The College of Science seeks a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.

We regret to inform that only shortlisted candidates will be notified.