About the Project
Ph.D. studentships are currently available for research on optimization and model predictive control methodologies for multi-scale systems. The project will place a particular focus on microscopic/stochastic systems with applications in industrial biotechnology and predictive microbiology. The project builds on the group’s years of experience on model reduction methodologies for optimisation and control of systems described by large sets of nonlinear partial differential equations and of multi-scale systems dynamically coupling macroscopic and micro/mesoscopic simulations.
See http://www.manchester.ac.uk/research/k.theodoropoulos/publications for a list of our relevant publications.
There are funding opportunities available based on the academic track-record of applicants, and evidence of research potential.
Competition for funds is expected to be extremely high.
Research will be supervised by Prof. Theodoropoulos and involves the development of novel algorithms based on model reduction techniques for systems ranging from the microscopic (molecular/atomic level) to the mesoscopic scale.
Candidates should ideally have an MSc and a 1st class BSc in Chemical Engineering or a related field such as Physics, Applied Mathematics, Physical Chemistry etc. and should have computational modelling experience and good knowledge of a programming language (FORTRAN and/or C/C++).
Successful candidates will be enrolled in the 3-year Ph.D. program of the School of Chemical Engineering and Analytical Science.
References
1. Bonis, I, Xie, W & Theodoropoulos, C 2014, 'Multiple model predictive control of dissipative PDE systems' IEEE Transactions on Control Systems Technology, vol 22, no. 3, 6566038, pp. 1206-1214. DOI: 10.1109/TCST.2013.2270182.
2. Bonis, I, Xie, W & Theodoropoulos, C 2012, 'A linear model predictive control algorithm for nonlinear large-scale distributed parameter systems' AIChE Journal, vol 58, no. 3, pp. 801-811. DOI: 10.1002/aic.12626.
3. Theodoropoulos, C 2011, Optimisation and linear control of large scale nonlinear systems: A review and a suite of model reduction-based techniques. in Lecture Notes in Computational Science and Engineering|Lect. Notes Comput. Sci. Eng.. vol. 75, Lecture Notes in Computational Science and Engineering, vol. 75, Springer Verlag, Berlin-Heidelberg, pp. 37-61, International Research Workshop: Coping with Complexity: Model Reduction and Data Analysis, Ambleside, 1 July. DOI: 10.1007/978-3-642-14941-2_3
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