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Together, we create amazing technology that unlocks access to energy for the benefit of all.
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Joint model learning, inversion and classification of NMR spectroscopy data
Scope of the internship:
The scope of internship is to work on classification of nuclear magnetic resonance (NMR) data of measured rock and fluid samples by combining mathematical physics (integral equations coming from physics of NMR and approximation theory) and machine learning methods (gradient descent based optimization and automatic differentiation libraries). NMR instruments directly measure the quantity of hydrogen atoms in a sample which provides a method for determining porosity and permeability in porous material as well as different sources of hydrogen in liquids. These techniques are used to determine the properties in geological formation as well as organic contents, making them essential in understanding the hydrocarbon reservoirs as well as origin and evolution of life in extra-terrestrial and heavenly bodies.
The candidate will be working on algorithm development, computational infracture for verification and validation of the algorithm. During the development of the algorithm, the candidate will be exposed to many levels of the process starting from concept to deployment to utilization. This will be performed in a multidisciplinary environment of industrial mathematicians, physicists, software engineers as well as field personnel. The results of the internship will be documented in terms of reports and codes.