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AI Driven Mesoscopic Material Design


Project Description

A fully-funded iCASE PhD position in AI Driven Mesoscopic Material Design is available in the Multiscale Modelling Group at the School of Chemical Engineering and Analytical Science (CEAS) at the University of Manchester, working with Dr Alessandro Patti, in collaboration with Dr Breanndán Ó Conchúir from IBM Research at Daresbury. The PhD project would ideally start by September 2019 or any time before this date by mutual arrangement. The research group focus is in the area of simulation of soft matter at an atomistic or coarse-grained level. For more information, please refer to Dr Patti’s and CEAS websites:

https://personalpages.manchester.ac.uk/staff/alessandro.patti/default.htm

https://www.ceas.manchester.ac.uk/research/themes/multi-scale-modelling/

The research project aims to apply AI driven intelligent simulations to the problem of “inverse design” of industrially relevant mesoscopic materials. More specifically, given a set of desired material properties, we aim to identify the formulation composition, concentration and processing characteristics required to reproduce these properties. The use-case will be colloidal formulations which self-assemble to form mesoscopic colloidal suspensions, gels and glasses. The outcomes of this analysis will be relevant to a wide range of industries enabling them to gain insight into the mechanisms underpinning the macroscopic properties of their products and suggest which changes in formulation would enhance their performance.

Candidates with a background in machine learning, physics, chemical engineering, maths, chemistry, materials, statistics, or computational science are encouraged to apply. Applications will be considered upon receipt and the position will remain open until filled.

Funding Notes

This project is funded by EPSRC and IBM Research under the programme EPSRC Industrial CASE.

How good is research at University of Manchester in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?
Chemical Engineering

FTE Category A staff submitted: 33.90

Research output data provided by the Research Excellence Framework (REF)

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