|CCL 17.07.06 PhD in Computational Approaches, University of Nottingham, School of Pharmacy, UK|
From: jobs at ccl.net (do not send your application there!!!)
To: jobs at ccl.net
Date: Thu Jul 6 06:53:10 2017
Subject: 17.07.06 PhD in Computational Approaches, University of Nottingham, School of Pharmacy, UK
Computational Approaches to Determining Biomaterial DesignRules:
A funded PhD studentship is available at the University of Nottingham. The successful candidate will be linked to the EPSRC (Engineering and Physical Sciences Research Council) Programme Grant in Next Generation Biomaterials Discovery. This studentship will be based in the School of Pharmacy. For more information on the Programme Grant please visit the website: Biomaterial Discovery
The way materials properties control cell response (such as adhesion, differentiation, growth, death) can be captured in quantitative structure-property relationships (QSPRs). Recent developments in mathematics have identified efficient feature selection and nonlinear mapping methods that are well matched to the problems of identifying complex QSPRs in the data generated in physical, chemical and biological experimental research. We are using this approach to map molecular and physicochemical features of materials derived computationally and from experiments to cell fate using regularized neural networks. Previously, we have shown that both computed descriptors and those derived from experiments can model the complex relationships between the polymer surface chemistry and the response of cells exposed to these materials. Although current computed molecular descriptors are effective they are difficult to interpret, making it difficult to infer what types of new materials need to be synthesized that will be more effective in modulating cell responses. To increase the ability of our models to provide insight into how materials properties drive cellular responses for new materials in a 3D environment, we need to discover improved computed molecular descriptors that better represent relevant physicochemical and biological properties of materials, and that are easier to interpret in terms of chemical structure and properties.
This project aims to significantly expand the suite of available computed molecular descriptors and use them for ins silico design of materials with bespoke, fit-for-function biological properties. We will use them to generate QSPR models from our diverse high throughput materials synthesis and characterization research. The project will use the extensive computational facilities available locally at the University of Nottingham and the new Tier-2 HPC Midlands Plus computational facilities.
Advanced biomaterials are an essential part of meeting healthcare challenges facing society, such as antimicrobial resistance and realizing the potential of regenerative medicine to treat chronic disease. This Programme Grant aims to realize the vision of materials screening and discovery in 3D. This will allow us to move beyond the existing limited range of bioresorbable polymeric drug and cell delivery agents and medical device materials to bespoke biomaterials identified for specific applications. This studentship will also link with existing CDTs (Centres for Doctoral Training) including: CTD in Advanced Therapeutics and Nanomedicine, CDT in Regenerative Medicine and CDT in Additive Manufacturing and 3D Printing.
Interested applicants should contact Phil Williams (Phil.Williams,,nottingham.ac.uk) and Elizabeth Hufton (Elizabeth.Hufton,,nottingham.ac.uk) for more information and to send them a copy of your application. To apply please complete the online application form available at:http://www.nottingham.ac.uk/pgstudy/how-to-apply/how-to-apply.aspx including a CV and quoting EPSRC PG PhD. The funded PhD project will be undertaken for the full 4 years of PhD funding with concurrent transferable skills training (includes tax free stipend: 14,553 in 2017/18). This opportunity is available with stipend and fees payable to UK or EU candidates.NOTE THAT E-MAIL ADDRESSES HAVE BEEN MODIFIED!!!
All @ signs were changed to ,, to fight spam. Before you send e-mail, you need to change ,, to @
For example: change joe,,big123comp.com to email@example.com
Please let your prospective employer know that you learned about the job from the Computational Chemistry List Job Listing at http://www.ccl.net/jobs. If you are not interested in this particular position yourself, pass it to someone who might be -- some day they may return the favor.
|Modified: Thu Jul 6 10:53:10 2017 GMT|
|Page accessed 879 times since Thu Jul 6 11:04:48 2017 GMT|