Applications are invited for a full-time researcher based in the Department of Civil and Environmental Engineering at Imperial College London to carry out research programmes in control engineering for the design and control of dynamically adaptive water supply networks. Urban water systems face major challenges in providing safe and reliable water supply due to increasing population (demand), ageing infrastructure and climate change. Dr Ivan…
Job listing information
- Reference ENG01152
- Date posted 2 January 2020
- Closing date 31 January 2020
Job description
Job summary
Applications are invited for a full-time researcher based in the Department of Civil and Environmental Engineering at Imperial College London to carry out research programmes in control engineering for the design and control of dynamically adaptive water supply networks.
Urban water systems face major challenges in providing safe and reliable water supply due to increasing population (demand), ageing infrastructure and climate change. Dr Ivan Stoianov and his InfraSense Labs team have pioneered advances in sensor and control technologies in order to gain extraordinary insights into the operation of complex water supply networks and facilitate their dynamically adaptive control. The research group is at the forefront of developing and implementing novel simulation and optimisation methods that make use of this new knowledge about the dynamics of large-scale water supply systems and the ability to robustly control their operation.
This post is part of a 5-year EPSRC-funded programme of work to progress the development of fundamental scientific methods for the design, optimisation and control of next generation resilient water supply networks that dynamically adapt their connectivity, hydraulic conditions and operational objectives. The current post is suitable for someone with a background related to the mathematics of robust and adaptive control.
Duties and responsibilities
- Support and co-lead fundamental and applied research in robust and adaptive control algorithms
- Work on performance evaluation and comparison of various control methods to meet multi operational objectives
- Produce scientific publications describing the methods and results in collaboration with internal and external researchers
- Work closely with Dr Ivan Stoianov and the team at InfraSense Labs to enhance and utilise current experimental research to generate benchmark models and validate the developed control algorithms in operational water supply networks
- Actively contribute to the development of control ideas for dynamically adaptive and resilient networks and to support the integration of these ideas with emerging grant proposals
Essential requirements
- PhD degree in a relevant field such as: control engineering, chemical process engineering, computer science and/or applied mathematics with in-depth knowledge of the mathematics of robust control; or equivalent research, industrial or commercial experience.
- Excellent programming skills (e.g. Matlab, Python, C/C++) with experience in software configuration management (e.g. revision control via GitHub)
- Practical experience of publication of research in high-impact journals
- A strong background in responsible data management in scientific research
Competence in high level mathematical analysis
Further information
For informal enquiries about the post please contact Dr Ivan Stoianov at [email protected].
Our preferred method of application is online via our website. Please click ‘apply’ below or go to https://www.imperial.ac.uk/job-applicants/ and search using reference number ENG01152.
Further information about the post is available in the job description.
Any queries regarding the application process should be directed to Briony Webb at [email protected].
For technical issues when applying online please email [email protected].
Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £35,477 – £38,566 per annum.
Closing Date: 31/01/20.
Documents
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Additional information
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