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Complete the following questionnaire for - Postdoctoral Fellowship in CFD Modelling and Simulation - Multiscale particle simulations in fluid dynamics using machine-learning techniques  (255 KB)



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.

The objective of this project is to explore the use of Machine Learning techniques to derive data-driven closure relations for efficient multiscale computations. In particular, the candidate will use advanced active learning strategies to couple mesoscopic and macroscopic particle-based descriptions of complex fluids using Dissipative Particle Dynamics and Smoothed Particle Hydrodynamics methods.

The postdoctoral candidate working on this project will interact both with the CFD Modelling and Simulation (Ikerbasque Research Prof. Marco Ellero) and the Machine Learning groups at BCAM. The project will be performed also in collaboration with Prof. P. Espaol (Department of Theoretical Physics, UNED Madrid).


Deadline: September 13th 2019, 14:00 CET. (UTC+1) .

Applications will be evaluated in a continuous manner, with a response period no longer than 4 weeks after the call deadline.

((required)) Compulsory field.

Call is CLOSED

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)