PhD Studentship in Optimal Decision Making under Uncertainty at Imperial College London
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PhD Studentship in Optimal Decision Making under Uncertainty at Imperial College London
PhD Studentship: Untaxed bursary of £15,590 per annum plus UK/EU fees. UK nationals are eligible. Non-UK EU Students who have been in UK for three or more years, fulfilling the EPSRC regulations, as stated in Official Website. will also be considered.The Department of Computing is a leading department of Computer Science among the UK Universities. It has consistently been awarded the highest research rating (5*) in Research Assessment Exercises and was rated as "Excellent" in the previous national assessment of teaching quality.
We have a research studentship starting in October 2011 to work on a project entitled “Robust Optimization of Nonlinear Processes under Uncertainty”. Robust optimization studies procedures for decision making under uncertainty over time. While deterministic optimization problems include only known parameters, real-life decision problems almost invariably involve uncertain parameters. The uncertainty may arise because of future/unobservable events, measurement errors, or lack of reliable data. Robust optimization has manifold applications in financial engineering, production planning, supply chain management, fleet management, melt control, resource allocation in networks, groundwater pollution control, project scheduling, catastrophic risk management, telecommunications, power system operation, valuation of electricity generation capacity, public infrastructure investment planning, farming, computer chip design and many more. In a vast number of application areas, robust optimization has been shown to be superior to most other modelling paradigms. Many industries have recognised the value of quantitative methods for decision making under uncertainty, and major industrial companies and financial institutions are continuously seeking experts in this field.
Dynamic robust optimization problems are very hard; models need to be formulated in a clever way to facilitate their efficient solution, and dedicated approximation schemes and solution algorithms must be designed. In this project we will develop robust local and global optimization methods for important decision problems involving nonlinear dynamic processes under uncertainty. Two high impact applications will also be investigated in collaboration with our industrial partners, and the developed methods will be applied to the integrated design, optimization and control of process systems. The appointed researcher is expected to conduct high quality research towards this aim. The project builds on current work in the research groups of Dr. D. Kuhn, Prof. B. Rustem and Prof. E. Pistikopoulos.
All applicants should have at least a Computer Science, Mathematics, Physics or related science degree, ideally with emphasis on theoretical and applied optimisation, operationsresearch, industrial or financial engineering or control. Applicants must demonstrate that they possess the following attributes:
- A high affinity to mathematical reasoning, strong analytical and numerical skills and a quick grasp of new concepts.
- Broad knowledge of optimisation and probability theory or the willingness to acquire such knowledge before or shortly after the start of the employment.
- Ability to write clearly and concisely to a level consistent with publications in journals of the highest level internationally.
Shortlisted candidates will be interviewed, and the successful candidate will be expected to make an (expedited) online application for PhD study according to normal College admission procedures.
Applications must include the following:
- A 2- 3 page research statement that describes what you see as interesting research issues relating to this studentship and what relevant experience you have.
- A detailed CV
- Transcripts of all degree results
- 2 recent letters of references
Applicants are advised to Visit Official Website. for general information on becoming a PhD student in the Department of Computing.
Closing date: 31st July 2011