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Complete the following questionnaire for - IC2019_Winter Postdoctoral fellow position in Deep Machine Learning and Probabilistic Modelling for the Inversion of Mechanical Engineering Problems  (315 KB)


Deep Machine Learning and Probabilistic Modelling for the Inversion of Mechanical Engineering Problems

The project is focussed on electromagnetic (EM) measurements that incorporate information about the resistivity distribution of the Earth ́s subsurface. Such information can be used to determine the porosity of the rocks and the type of fluids contained within those rocks. The correct interpretation (numerical inversion) of the measurements is critical for obtaining an accurate map of the Earth ́s subsurface.

The main objective of this Project is to implement and analyse the new Bayesian inversion methodologies proposed in BCAM for the efficient inversion of geophysical EM resistivity measurements. Such methodologies rely on advanced importance sampling techniques and adaptive numerical schemes. The possibility to apply the new methods in machine learning algorithms will be also investigated.

Keywords: Inverse Problems with Uncertainty, Geophysical Electromagnetic Resistivity Measurements, Bayesian Inference, Enhanced Sampling, Hamiltonian Monte Carlo, Importance Sampling, Deep Machine Learning



PI: Elena Akhmatskaya http://www.bcamath.org/en/people/eakhmatskaya
PI: David Pardo http://www.bcamath.org/en/people/dpardo


Deadline: Feb 28th, 2019 15:00 CET (UTC+1)


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Eusko Jaurlaritza - Gobierno Vasco ikerbasque - Basque Foundation for Science Bizkaia xede. Bizkaiko Foru Aldundia innobasque - Agencia vasca de la innovación Universidad del PaÌs Vasco (UPV/EHU)