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PhD studentship at Cambridge in machine learning ... (No replies)
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An EPSRC funded PhD studentship is available in the Engineering Laboratory at the University
of Cambridge for a 2019 October start. Deadline: the position is now open until filled!
This project is a collaboration between Cambridge and Newcastle Universities and AstraZeneca
(via the CASE programme), and will focus on the design of novel machine
learning based force fields for use in computer-aided drug discovery.
Accurate computational methods for predicting the strength of binding between a candidate
drug molecule and its therapeutic target have the potential to revolutionise the drug
discovery process. Our goal is to improve the accuracy of this approach by combining
free energy calculations with the Gaussian Approximation Potential (GAP), which
employs machine learning techniques to faithfully reproduce the quantum mechanical potential
energy surface of the drug molecule.
This project will be coordinated by two supervisors with expertise in machine learning
(Prof Gábor Csányi, University of Cambridge) and computer-aided drug design (Dr Daniel
Cole, Newcastle University) aspects of this research. The student will work closely
with drug discovery programmes at AstraZeneca (Dr Graeme Robb) with the goal of establishing
these computational methods as part of the standard tool kit in the drug discovery pipeline.
The project will provide highly sought-after training in the fields of computational
medicinal chemistry and machine learning. As such, it will provide excellent experience
for a future career in either academia or the pharmaceutical industry.
Funding rules of EPSRC stipulate that applicants must be EU citizens resident in the UK (for at least the last three years). Informal enquiries should be directed to Prof Gábor Csányi ([email protected])
Further information on research at Cambridge, Newcastle and AstraZeneca may be found at:
http://www.eng.cam.ac.uk/profiles/gc121
https://blogs.ncl.ac.uk/danielcole/
http://www.astrazeneca.co.uk
https://www.csc.cam.ac.uk/academic/cdtcompmat