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Research Associate in Bayesian Models for Synthetic Trials

University of Sheffield - Faculty of Science - School of Mathematics and Statistics

Location: Sheffield
Salary: £31,302 per annum, Grade 7
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 9th October 2018
Closes: 12th December 2018
Job Ref: UOS020573

Contract Type: Fixed term for 36 months

The School of Mathematics and Statistics has a strong research ethos and an international reputation in research, with research groups in Algebra and Algebraic Geometry, Differential Geometry, Fluid Dynamics, Mathematical Biology and Environment, Number Theory, Particle Astrophysics and Gravitation, Probability, Solar Physics, Statistics, and Topology. There are also fruitful interdisciplinary links across the University.

We are seeking to appoint a Research Associate to provide in silico-augmented clinical trials for Tuberculosis (TB) vaccination development. You will focus on the development of statistical models based on mechanistic simulator which allow for uncertainty propagation through its components. You will lead a programme of work to combine Phase II clinical trials data with in-silico data from an agent-based simulator to build an adaptive Bayesian clinical trial capable of potentially reducing the size and duration of the trial in humans will draft papers for scientific journals based on these results.

You will hold a PhD in a relevant discipline (or have equivalent experience).

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

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