postdoctoral positions for promising researchers to join our research groups Basque Center for Applied Mathematics Spain

JOB DETAILS
Title 5 postdoctoral positions for promising researchers to join our research groups
Employer BCAM - Basque Center for Applied Mathematics
Job location Alameda de Mazarredo 14, 48009 Bilbao, Bizkaia
Published August 19, 2019
Application deadline September 13, 2019
Job types Postdoc
Fields Materials Engineering, Algorithms, Energy Technology, Biomedical Engineering, Clinical Sciences, Geophysics, Artificial Intelligence, Health Science, Fluid Dynamics


JOB DESCRIPTION


We have also launched 5 postdoctoral positions for promising researchers to join our research groups and work on the following topics, related to Artificial Intelligence:

 

1) Postdoctoral Fellowship in Simulation of Wave Propagation: Deep Learning Based Inversion with Energy Applications

Applications are invited for a postdoctoral position within the Simulation of Wave Propagation group at BCAM. The project, entitled "Deep Learning Based Inversion with Energy Applications", deals with Solving inverse problems in computational mechanics using deep learning algorithms with applications to geophysics.

More info: http://www.bcamath.org/en/research/job/postdoctoral-fellowship-in-simulation-of-wave-propagation

2) Postdoctoral Fellowship in Metabolic Modelling: Predictive metabolic modelling of microbiomes and human metabolism through Monte Carlo sampling

Applications are invited for a postdoctoral position within the Modelling and Simulation in Life and Materials Sciences group at BCAM. The project "Predictive metabolic modelling of microbiomes and human metabolism through Monte Carlo sampling" is run in collaboration with the Quantitative Metabolic Modeling group at Berkeley National lab (LBNL).

More info:http://www.bcamath.org/en/research/job/postdoctoral-fellowship-in-metabolic-modelling

3) Postdoctoral Fellowship in Machine Learning Driven Atomistic Simulations for Energy & Health

The project "Machine-Learning-Driven Atomistic Simulations for Energy and Biomedical Applications" will be led by the group of Modelling and Simulation in Life and Material Sciences at BCAM (Basque Country) and the MS2Discovery Interdisciplinary Research Institute at Wilfrid Laurier University (Waterloo, Canada). Both groups are involved in the International Consortium on Multiscale Modelling of Advanced Energy Materials and collaborate extensively with physicists, mathematicians, theoretical/experimental chemists and engineers from a number of institutions around the world. The objective of the aforementioned project is to enable efficient and tractable simulations of several important classes of complex atomistic systems through the use of novel Machine Learning (ML) techniques, paying particular attention to those cases where state of the art Molecular Dynamics (MD) algorithms are lagging behind the current needs of challenging applications in energy and health.

More info: http://www.bcamath.org/en/research/job/postdoctoral-fellowship-in-machine-learning-driven-atomistic-simulations-for-energy

4) Postdoctoral Fellowship in Artificial Intelligence in prediction for clinical practice

This fellowship will deal with research projects of health services research in chronic diseases that are currently being carried out at the Galdakao-Usansolo Hospital which would integrate complex techniques of data analysis and artificial intelligence in their development.

More info:http://www.bcamath.org/en/research/job/postdoctoral-fellowship-in-artificial-intelligence-in-prediction-for-clinical-practice

5) Postdoctoral Fellowship in CFD Modelling and Simulation - Multiscale particle simulations in fluid dynamics using machine-learning techniques

This project deals with the multiscale simulation of complex fluids/materials using data-driven closure models obtained through active learning techniques. In particular, the governing equations describing the macroscopic flow of complex fluids - such as polymer-colloidal suspensions etc – generally involve a significant degree of physical approximations, which make continuum constitutive models valid only in a limited subclass of flows.

More info: http://www.bcamath.org/en/research/job/postdoctoral-fellowship-in-cfd-modelling-and-simulation-multiscale-particle-simulations-in-fluid-dynamics-using-machine-learning-techniques


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