Research Fellow (Machine Learning)

School of Physical and Mathematical Sciences (SPMS) invites applications for a Research Fellow position to develop state-of-the-art machine learning technologies to analyze seismic data.

By leveraging the novel machine learning techniques, later seismic phases which imposes unique constraints on earthquake location and subsurface structure will be reliably identified, and hence improving the accuracy and precision of earthquake locations and subsurface monitoring.

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.

Key Responsibilities:

• Gather seismic data in fields
• Develop novel machine learning approaches for seismic data processing, especially later seismic phase identification
• Improve and develop the state-of-the-art earthquake location methods to precisely locate earthquakes
• Publish high-quality scientific results in top peer-reviewed journals

Job Requirements:

• PhD in mathematics, geophysics, seismology, computer sciences, or equivalent
• Evidence of innovation and/or high-quality publications in computer sciences, computational & applied mathematics, geophysics, seismology, or physics
• Enthusiasm, good communication skills, and able to conduct independent research

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