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Call Postdoctoral fellow positions

Complete the following questionnaire for - Postdoctoral Fellowship in Machine Learning Driven Atomistic Simulations for Energy & Health  (258 KB)

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. Many such applications require the modelling based on local atomic interactions for better understanding properties of the underlying structures and systems. Such interactions, often derived from quantum mechanical representations, are prohibitively expensive to simulate. ML algorithms provide a natural tool to address this challenge, and thus one of the underlying ideas of the project is to train neural networks efficiently or to use other ML tools in order to reproduce results of density functional theory calculations at a much lower cost.

The postdoctoral candidate will work under the supervision of Ikerbasque Research Professor Elena Akhmatskaya (MSLMS group, BCAM) and Roderick Melnik (Wilfrid Laurier University).

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.

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Complete if (expected to be) finished before your expected starting date at BCAM


The highest non-PhD degree (expected to be) finished before your expected starting date at BCAM

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Curriculum Vitae

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Interest letter

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NOTICE: Once you have applied to the position, you may not re-apply in this position. Please make sure that everything is correct.

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