EngD Studentship in Computationally Efficient Hydraulic Modelling for Cyber-Physical Water Systems

This project investigates computationally efficient hydraulic analysis models for water supply networks. A prerequisite for the design and implementation of Cyber-Physical Water Systems which include near real time analysis, leak detection, and control is the formulation of computationally efficient hydraulic models of the network and its physical elements. The research will encompass various topics in modelling, model identification or calibration, and linear and nonlinear solvers for large scale hydraulic systems. The work forms an integral part of research in Cyber-Physical Water Systems carried out by InfraSense Labs at Imperial College London.

This is a joint four year industry driven STREAM (http://www.stream-idc.net/) project with Bristol Water (http://www.bristolwater.co.uk/about-us/the-company/). The successful candidate is expected to spend the first two years at Bristol water (in the city of Bristol) working with the water network analysis team. The work there will combine both fundamental research and learning the complexity and challenges of modelling operational systems. As part of the STREAM activities, the student will also attend taught courses relevant to the research programme. In the last two years the student will be based in Imperial College London working closely with a multidisciplinary team of researchers in InfraSense Labs led by Dr Ivan Stoianov. The immediate research group is currently composed of two post-doctoral research associates and seven PhD students working on various aspects of cyber-physical water systems. Examples include but are not limited to: fast optimization algorithms for water networks; optimal control and estimation for water networks; modelling and scheduling for pumps; network sensor technology; field-based data acquisition and software infrastructure; embedded stream processing; architecture, communica- tion, and protocols for cyber-physical systems; big-data mining, autonomous learning and complex event recognition; graph theory and adaptive water network topology.

The candidate will have the opportunity to develop and adapt hydraulic solvers suitable for large network problems. There will also be an opportunity to develop relevant algorithms in Matlab, Python, C or R. There will also be a chance to learn from the technical expertise of our dynamic, multidisciplinary team of researchers and collaborators.

ELIGIBILITY

Applicants for the studentship should have or expect to obtain a first or upper second class honours degree or equivalent in a relevant aspect of Mathematics, Physics, Engineering, Computing, or other strongly quantitative discipline. Good computing skills are required. A Master's degree in Hydraulic Engineering, Applied Mathematics, or Computer Science or related subjects would be advantageous but is not mandatory. It would also help if you have a background in the following areas, or have a background indicating that you can acquire these skills:

Linear Algebra, Numerical Methods for Solving Linear Equations.
Matlab, and/or C, Python, R.