Qualification Type: | PhD |
---|---|
Location: | Nottingham |
Funding for: | UK Students |
Funding amount: | Competitive Funding (UK) |
Hours: | Full Time |
Placed On: | 26th February 2024 |
---|---|
Closes: | 31st May 2024 |
Reference: | ENG1753 |
This 36-month funded PhD studentship will contribute to cutting-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative methods such as high-throughput experimentation to expediate the syntheses (and bioanalysis) of life-saving pharmaceuticals. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. The research will be conducted using state-of-the-art equipment, including both commercial tools and bespoke in-house apparatus. As a key member of our team, you will play a pivotal role in advancing the frontiers of drug discovery, laboratory automation, and the modelling of chemical data.
Key Responsibilities:
Qualifications:
Application Process:
To apply, please submit your CV and a cover letter outlining your research interests and relevant experience to Connor.Taylor@nottingham.ac.uk. Please also contact this email for further information and an informal discussion regarding the PhD.
This is an excellent opportunity for an enthusiastic graduate to build a strong skillset in interdisciplinary research and a collaborative network with both academic and industrial partners at an international level. Due to the nature of the funding, only UK applicants can be considered for this position - upon finding the successful candidate, funding is then acquired through University of Nottingham.
Type / Role:
Subject Area(s):
Location(s):