Become a sponsor to Robin Thibaut
About
I am a PhD Fellow at Ghent University, Belgium. My research focuses on developing a new framework for experimental design in earth sciences under a Bayesian approach. I have experience in marine geophysical surveys, near-surface geophysics and coding. My research interests include Bayesian statistics, geostatistics, image processing, data science, and machine learning. I am interested in applying these methods to a variety of problems in environmental sciences and earth sciences.
Latest papers
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
https://doi.org/10.1016/j.jhydrol.2021.126903
- Official repository: skbel
A new workflow to incorporate prior information in minimum gradient support (MGS) inversion of electrical resistivity and induced polarization data.
https://doi.org/10.1016/j.jappgeo.2021.104286
- Official repository: MGS-public
Research
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Reaching this goal will help me to be more independent and will help me to spend more time on my open source projects and more time on improving my programming skills and my knowledge. I will be able to work on my passion, programming, without any financial stress.
Featured work
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scikit-learn/scikit-learn
scikit-learn: machine learning in Python
Python 60,557 -
scikit-fmm/scikit-fmm
scikit-fmm is a Python extension module which implements the fast marching method.
Python 282 -
robinthibaut/pysgems
Use SGeMS (Stanford Geostatistical Modeling Software) within Python.
Python 46 -
robinthibaut/skbel
SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
Python 24
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