|Funding for:||UK Students, EU Students|
|Funding amount:||£14,777 maintenance stipend, Home/EU tuition fees, and a training support fee of £1,000 per annum|
|Placed On:||6th February 2019|
|Closes:||30th April 2019|
Lead Supervisor: Dr Bernardo Castro Dominguez
Project enquiries: firstname.lastname@example.org
Please include in your correspondence your motivation for applying to this project and the reasons why you feel you are a suitable candidate.
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.
Formal applications should be made via the University of Bath’s online application form for a PhD in Department of 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:
This project is eligible for inclusion in funding rounds scheduled for end of 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 up to 3.5 years. Early application is strongly recommended.
Expected start date: 30th September 2019
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