Applications are invited for an EPSRC Industrial CASE PhD studentship with GlaxoSmithKline, which aims to develop mathematical approaches to the optimisation of drug discovery processes. The project will be concerned with abstractions of this process using, for example, multi-fidelity stochastic bandit models. Insights from the mathematical analysis of these models will be used to recommend good practices for molecular screening and lead optimisation. This will involve a theoretical study of the frequency properties of Bayesian optimisation techniques, retrospective analyses of datasets generated by project teams at GlaxoSmithKline, and the implementation of novel protocols for drug discovery and development.
GlaxoSmithKline will host the student at their laboratory in Stevenage for three months during the studentship where training will be provided in various aspects of the company's drug discovery research and development processes. The student will have the opportunity to test their methodologies retrospectively on real datasets and develop new experimental design approaches for drug discovery. Further information can be obtained from the project supervisors Dr Sergio Bacallado (email@example.com) at Cambridge and Dr Nicola Richmond (firstname.lastname@example.org) at GlaxoSmithKline.
Applicants should have (or expect to obtain) a 1st class, or high 2:1, honours degree (and preferably a Masters degree) in Mathematics or a closely related discipline. A knowledge of statistics and machine learning would be advantageous.
This EPSRC funded PhD studentship is available for Home and EU students. Home students and certain EU students will receive a full studentship including fees and maintenance at the current research council rate. EU students will receive a fees only award. Details on eligibility can be found on the EPSRC web site: https://www.epsrc.ac.uk/skills/students/help/eligibility/. Overseas students are not eligible and should not apply. The length of funding will be 3 years in the first instance.
Applications should be emailed to Dr Sergio Bacallado (email@example.com). Please include (i) a research statement outlining your suitability, why you are interested in pursuing a PhD in this area, your background and research interests, (ii) your CV stating your citizenship and years of residence in the UK (iii) copies of your academic transcripts and (iv) contact details for two academic referees.
Please quote reference LF13320 on your application and in any correspondence about this vacancy.
The closing date for applications is January 15, 2018. Interviews will be held week commencing February 1, 2018.
The successful applicant will be expected to formally apply for admission through the University's Graduate Admissions Office.
The University values diversity and is committed to equality of opportunity. The Department would particularly welcome applications from women, since women are, and have historically been, underrepresented on our student cohort.
The University has a responsibility to ensure that all students are eligible to live and work in the UK.