Function and Responsibilities:

• Applied mathematician performing gravity field retrieval simulations.
• Development of post-processing environments to evaluate simulations.

Research Interests:

• Gravity field retrieval
  
• Data science and Applied Machine Learning with scarce data
• High performance computing
• Post-processing and visualization of results

Career:

Education:

1999-2002 Bachelor of Science (B.Sc.), University of Stellenbosch

2002-2004 B.Sc. Hons, University of Stellenbosch 

2004-2006 Master in Engineering Science, University of Stellenbosch

2006-2012 PhD, Computational fluid dynamics, University of Stellenbosch

Projects:

March 2021 - Present

• Gravity field retrieval simulations with EPOS-OC for NGGM/MAGIC – Science Support Study and ESA Third Party Mission Study.
• Support for development of Payload Data Ground Segment concept for the NGGM study.
• Gravity field retrieval simulations and development of post-processing strategies for MAGIC_CCN1
• Gravity field retrieval simulations and further development of post-processing strategies for NGGM-MPEF

March 2019 – February 2021

• Development of SNAP pipeline in python for the calculation of land subsidence from SAR Sentinel1 images.

• Modelled suspended particle matter in the Wadden sea using stacked ensemble machine learning methods.

• Developed Flask based API for doing pre-processing for chemical and biological input components to the DFlowFM model.

• Developed and deployed docker containers for Jupyter notebooks on Azure. These notebooks were for online courses at Deltares.

• Developed a gradient boosting based machine learning model to predict overtopping at coastal structures.

December 2015-February 2019

• Developed a river routing model to simulate the movement of surface runoff to oceans on a global scale. 

• Constructed an interface that converts weather data from three different sources into a standard format and combines these into a single database. 

• Constructed models to predict overtopping at coastal structures with OpenFOAM (IHFoam).

• Trained feedforward and recurrent neural networks on river discharge data.