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Applied Machine Learning: Towards the Rapid Discovery of New Drugs

Project Description

To cope with our energy, environmental and health requirements, advances in materials innovation need to be accelerated in an economic and sustainable manner. Currently, this innovation is based on extensive material/process screening that demand significant time and resources (economic and human). As a smarter approach, machine learning is identified as a tool to accelerate materials innovation, predict material performance and obtain well-defined manufacturing procedures for their fabrication.

This PhD position comprises the field of applied machine learning for the discovery of solid-state pharmaceutical products. In specific, this project aims at utilising machine-learning tools to identify the combinatorial materials/processing properties that give rise to products, effectively accelerating the discovery and deployment of new drugs. In this project, the PhD candidate will develop the computational models and experimentally validate the predictions.

Applicants for this post must hold undergraduate Masters and/or MSc degree in one or more of the following: science (mathematics, computer science, chemistry, materials, etc.) and/or engineering (chemical, materials, mechanical, etc.) Applicants should have a keen interest in interdisciplinary research and in the discovery of new materials, pharmaceutical products and manufacturing technologies.

Informal enquiries should be directed to Dr Bernardo Castro (). Please include in your correspondence your motivation for applying to this project and the reasons why you feel you are a suitable candidate.

Formal applications should be made via the University of Bath’s online application form for a PhD in Chemical Engineering. Please ensure that you state the full project title and lead supervisor name on the application form.

More information about applying for a PhD at Bath may be found here:

Anticipated start date: 30 September 2019

Funding Notes

This project is eligible for inclusion in funding rounds scheduled for end of November 2018, January 2019, February 2019, March 2019 and April 2019. A full application must have been submitted before inclusion in a funding round.

Funding will cover Home/EU tuition fees, a maintenance stipend (£14,777 pa (2018/19 rate)) and a training support fee of £1,000 per annum for 3.5 years.

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

FTE Category A staff submitted: 61.00

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

Click here to see the results for all UK universities

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