The School of Civil and Environmental Engineering invites applications for our Research Fellow position.

The main purpose of the project is to establish a 3D Geological and Geotechnical Data Modelling and Management System. The Research Fellow is expected to characterize the spatial variability of geological and geotechnical properties based on data from more than 50, 000 boreholes so that the users can know the uncertainty of the established system. Random field model is going to be used to describe the spatial variability of the geodata and a zonation will be conducted to differentiate the areas with different levels of geological uncertainties. Additionally, artificial intelligence (AI) methods are going to be developed to facilitate the construction of the 3D geological model. The AI methods will translate geological knowledge into modeling rules and are expected to replace human work. In this way, the 3D model can be updated automatically when new borehole/geophysical data are available in the future.

The Research Fellow will work on this project to conduct researches on geostatistical analysis of the geotechnical and geological data. The roles of this position include:

• Characterize the uncertainty and spatial variability of the geological and geotechnical data based on large amounts of borehole data
• Spatially predict the geological and geotechnical properties using geostatistical methods such as the conditional random field and kriging method
• Spatially predict the geological and geotechnical properties using machine learning methods such as the neural network and support vector machine methods
• Write project reports and scientific research papers for publication, present research outcomes in relevant meetings/conferences
• Other academic-related activities such as funding proposal preparation and student supervisions
• Other administrative activities such as project coordination

Job Requirement:

• Ph.D. in Geotechnical Engineering, Geological Engineering or related field
• Knowledge in the random field, geostatistical method, artificial intelligence method and reliability analysis of geotechnical engineering
• Experiences in statistical and spatial variability analyses of soil/rock properties using basic programming languages such as MATLAB or R
• Experiences in analyses of big data using artificial intelligence method
• Experiences in reliability analysis of geotechnical structures
• Publication track record is an advantage
• Proficiency in English

We regret that only shortlisted candidates will be notified.