Research Associate in Computational Optimisation

Imperial College London - Department of Civil & Environmental Engineering

(Dynamically Adaptive Water Supply Networks)

Fixed Term appointment for up to 36 months (with the possibility of extending the contract)

Imperial College London is a science-based institution with the greatest concentration of high-impact research of any major UK university.

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 (www.imperial.ac.uk/infrasense), Civil and Environmental Engineering Department at Imperial College London, are at the forefront of developing and implementing novel simulation and optimisation methods, and technologies to dynamically and robustly control the connectivity and operation of large scale water supply networks.

This post is part of a 5-year EPSRC-funded programme of work (led by Dr Ivan Stoianov, http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/P004229/1) to progress the development of fundamental scientific methods for the design, optimisation and control of next generation resilient water supply networks with dynamically adaptive connectivity and operation. This post is most suitable for someone with a background related to computational optimisation (and/or applied mathematics).

Education, Skills and Knowledge

  • PhD degree (or equivalent) in a relevant field such as: applied mathematics, computer science, civil engineering and/or control engineering with in-depth knowledge of optimisation techniques, nonlinear programming (including mixed integer nonlinear programming) and sparse matrix algorithms.
  • Proven ability to publish research in high impact journals.
  • Excellent programming skills (e.g. Matlab, Python, C/C++) with knowledge of software configuration management (e.g. GitHub).
  • Work in a planned way, and implement responsible data, information and software management associated with the research programme.
  • Integrate with the interdisciplinary team of researchers in pipe hydraulics, control engineering and mathematical analysis; and contribute to the mathematics of nonlinear programming and sparse matrix algorithms for modelling the hydraulics of large scale water supply networks.

Main Duties and Responsibilities

  • Support and co-lead fundamental and applied research in mixed-integer non-linear optimisation methods, which also include large-scale sparse numerical optimisation methods and decision making under uncertainty, for the design and operation of dynamically adaptive water supply networks (complex networks).
  • Work on numerical iterative methods and pre-conditioners for solving large sparse systems of nonlinear equations for modelling the hydraulic behaviour of large scale water supply networks.
  • Work on the development of innovative and tailored optimisation algorithms.
  • Evaluate the performance of various optimisation approaches to meet multi operational objectives for the design and operation of complex water supply networks, produce scientific publications describing the methods and results in collaboration with internal and external researchers.
  • Work with other RAs and PhD students from the InfraSense Labs research group to enhance and utilise current experimental research in order to validate the developed optimisation methods in operational water supply networks.
  • Contribute to the development of modelling and optimisation ideas for dynamically adaptive and resilient networks, and strengthen the related interdisciplinary research activities of the InfraSense Labs research group.

For enquiries about the post please contact Dr Ivan Stoianov: (ivan.stoianov@imperial.ac.uk).

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £32,380 - £34,040 per annum

Our preferred method of application is online via our website. Please visit https://www.imperial.ac.uk/job-applicants/ (Select “Job Search” then enter the job title or vacancy reference number into “Keywords”). Please complete and upload an application form as directed quoting reference number EN20170352LE.

Share this job
     
  Share by Email   Print this job   More sharing options
We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

Location(s):

London