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This project, while fully funded by BU, will be executed in the framework of a large EU funded INFER project (www.infer.eu).
The INFER project research programme focuses on pervasively adaptive software systems for the development of an open, modular software platform for predictive modelling applicable in different industries and a next generation of adaptive soft sensors for on-line prediction, monitoring and control in the process industry. One of the main goals of the project is the promotion of a paradigm shift from human labour and knowledge intensive processes of building predictive systems to autonomous, evolving complex systems.
This project will combine the study and development of meta-learning approaches and techniques focusing on knowledge acquisition, representation and transfer for effective learning based on solving multiple predictive problems together with the research into complex learning systems analysed at the systems level with a focus on multi-component, multi-level, multi-objective optimisation and mechanisms (i.e. diversity, pool sizes, dynamics of adaptation, interactions, competition versus collaboration etc.).
The successful applicant will join the Computational Intelligence Research Group within the Smart Technology Research Centre and will be primarily based in the School of Design, Engineering & Computing at BU but will also be required to frequently visit and work at our commercial partners' labs (REC in Poland and Evonik Industries in Germany) providing an outstanding opportunity to gain a diverse experience of both academic and commercial environments.
Funding Notes:
The studentship carries a basic remuneration of £13800 pa tax-free and a waiver of the full-time research student fee. There are no restrictions on the nationality of the applicants and the selection will be based on the candidate's qualifications and experience.
Applicants should have a very strong mathematical background and hold a first or upper second class honours degree or equivalent in computer science, mathematics, physics, engineering, statistics or a similar discipline. Additionally the candidate should have strong programming experience using any or combination of C++, Matlab or Java.