Research Fellow (Machine Learning for Coastal Ocean Modelling)

The successful candidate will work with Dr. Pavel TKALICH on Coastal Hydrodynamic, Ocean Dynamics, Water Quality and Ecosystem Modelling, Environmental Impact Assessment, under soon-to-be funded projects on Dispersion of Pollutants in Singapore Coastal Waters and South-East Asia.

The main responsibilities of the positioninclude:

Development and application of Machine Learning/Data Driven methods for fast solution of broad range of Coastal, Ocean and Environmental problems.

Qualifications / Discipline:

Ph.D. or Master's degree from reputable universities in Oceanography, Coastal Science and Engineering, Data Science, or other related disciplines. Level of appointment and remuneration commensurate with the candidate's experience.

Skills:

• Development and implementation of machine learning / data driven models to analyze and interpret coastal oceanographic data.
• Experience may include: (a) Machine Learning, like decision trees, support vector machines, and neural networks (b) Statistical Analysis, including as regression analysis, hypothesis testing, and ANOVA (c) Data Mining, including clustering, association rule mining, and anomaly detection.
• Contribution to the advancement of predictive data driven models for coastal ocean processes, including wind-waves, currents, sea level extremes, coastal erosion, and marine ecosystem dynamics.