|Placed On:||16th January 2019|
|Closes:||24th February 2019|
Contract Type: Open Ended
Work Hours: 35.0 Hours per Week
Division/School: School of Computer Science, Electrical and Electronic Engineering and Engineering Maths
We seek a highly motivated Research Associate who is interested in working as part of a team at the interface between Engineering and Applied Mathematics to investigate new methods for exploring the nonlinear behaviour of engineered systems. The post will run until 31 May 2020, funded by an EPSRC grant with the possibility of an extension subject to funds and EPSRC permission.
Modern test methods for investigating the dynamics of engineered structures are inadequate for dealing with the presence of significant nonlinearity since they have largely been developed under the assumption of linear behaviour. In contrast, control-based continuation (CBC), a versatile non-parametric identification method, has been developed with nonlinearity in mind from the beginning. It has been demonstrated on simple experiments but now advances in underlying methodology are required to apply CBC to real-world experiments which have higher levels of measurement noise and many degrees of freedom. The versatility of CBC is such that, with these advances, it will also become relevant for researchers studying nonlinear systems in both engineering and other fields, such as in the biological sciences.
We are seeking a Research Associate to drive this research forward alongside other researchers (both PhD students and other post-doctoral staff) who are working on closely related problems. Support will be readily available from the investigators David Barton, Simon Neild and Djamel Rezgui. More widely, you will be part of the Dynamics and Control research group and the Applied Nonlinear Mathematics research group both of which carry out cutting-edge research in a wide range of application areas.
CBC presently draws on a wide range of underlying areas including, but not limited to, dynamical systems and bifurcation theory, control theory, system identification, and machine learning. Applicants are expected to have experience in at least one of these areas in addition to a first degree and preferably a PhD in Applied Mathematics/Physics/Engineering (or a closely related discipline).
Possible initial avenues of research include
Please direct any questions to David Barton, firstname.lastname@example.org. Apply online via the University of Bristol jobs website.
We appreciate and value difference, seeking to attract, develop and retain a diverse mix of talented people that will contribute to the overall success of Bristol and help maintain our position as one of the world’s leading universities.
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