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Position: Research Assistant/Associate in Inverting Turbulence: Flow Patterns and Parameters from Sparse Data
Institution: Imperial College London
Location: London, United Kingdom
Duties: Perform DNS (or LES) of turbulent flow and scalar dispersion around a building; Formulate and implement a stable data-assimilation algorithm; Examine accuracy, stability, and convergence properties of the algorithm; Draft journal papers; Attend relevant workshops and present at conferences
Requirements: Those appointed at Research Associate level Hold a PhD (or equivalent) in Engineering or a closely related discipline. Those appointed at Research Assistant level A first/masters degree (or equivalent) in Engineering
   
Text: Research Assistant/Associate in Inverting Turbulence: Flow Patterns and Parameters from Sparse Data Apply now Save this job Job summary Our ability to compute turbulent flows with scale-resolving simulations, like Large Eddy and Direct Numerical simulations, has grown tremendously in the past decades. In these simulations, problem parameters and boundary conditions are specified, and the forward problem is solved. In many real-life settings however, this information maybe uncertain or not available at all. For many of these turbulent flows observational data, such as... Job listing information Reference ENG02367 Date posted 15 November 2022 Closing date 14 December 2022 Key information about the role Location South Kensington Campus - Hybrid Position type Full time, fixed term Salary £38,194 - £50,834 plus benefits Department Department of Aeronautics Category Researcher / Non Clinical Researcher Job description Job summary Our ability to compute turbulent flows with scale-resolving simulations, like Large Eddy and Direct Numerical simulations, has grown tremendously in the past decades. In these simulations, problem parameters and boundary conditions are specified, and the forward problem is solved. In many real-life settings however, this information maybe uncertain or not available at all. For many of these turbulent flows observational data, such as velocity or scalar measurements, are available at several (static or moving) sensor locations. These observational data can be assimilated with the governing equations to recover the missing information. This is known as the inverse problem and in this sense, turbulence is "inverted". Many approaches have been proposed to solve the (ill-conditioned) inverse problem that can be broadly classified into two large categories: optimisation methods (such as data assimilation) and probabilistic methods (Bayesian inference). These methods are however either very time consuming or quickly become unstable for turbulent flows (due to the so- called "butterfly effect"). In this project, we aim to break the impasse by formulating a new data-assimilation algorithm, which is stable when applied to turbulent flows, and has affordable computational cost. We will apply the new approach to an environmental problem, flow and pollutant dispersion around a building. Success in this endeavour can open a new direction of research with many applications in other single or multi-phase flow problems. Duties and responsibilities Perform DNS (or LES) of turbulent flow and scalar dispersion around a building Formulate and implement a stable data-assimilation algorithm Examine accuracy, stability, and convergence properties of the algorithm Draft journal papers. Attend relevant workshops and present at conferences Essential requirements Those appointed at Research Associate level Hold a PhD (or equivalent) in Engineering or a closely related discipline. Those appointed at Research Assistant level A first / masters degree (or equivalent) in Engineering. *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant. Further information This is a full time, fixed term role for 17 months, based at South Kensington Campus. Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £38,194 - £41,388 per annum. For queries regarding the recruitment process please contact Lisa Kelly: l.kelly@imperial.ac.uk The College is currently trialling a Work Location Framework until early 2023. Hybrid working may be considered for this role and the role holder may be expected to work 60% or more of their time onsite, with 40% the minimum time spent onsite. The opportunity for hybrid working will be discussed at interview. The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/ The College believes that the use of animals in research is vital to improve human and animal health and welfare. Animals may only be used in research programmes where their use is shown to be necessary for developing new treatments and making medical advances. Imperial is committed to ensuring that, in cases where this research is deemed essential, all animals in the College’s care are treated with full respect, and that all staff involved with this work show due consideration at every level. http://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-integrity/animal-research Documents Job Description Research Assistant-Associate ENG02367.pdf About Imperial College London Imperial College London is the UK’s only university focussed entirely on science, engineering, medicine and business and we are consistently rated in the top 10 universities in the world. You will find our main London campus in South Kensington, with our hospital campuses located nearby in West and North London. We also have Silwood Park in Berkshire and state-of-the-art facilities in development at our major new campus in White City. We work in a multidisciplinary and diverse community for education, research, translation and commercialisation, harnessing science and innovation to tackle the big global challenges our complex world faces. It’s our mission to achieve enduring excellence in all that we do for the benefit of society - and we are looking for the most talented people to help us get there. Additional information Please note that job descriptions cannot be exhaustive, and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities. Imperial College is committed to equality of opportunity and to eliminating discrimination. All employees are expected to follow the Imperial Values & Behaviours framework . Our values are: Respect Collaboration Excellence Integrity Innovation In addition to the above, employees are required to observe and comply with all College policies and regulations. We are committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We therefore encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender reassignment, sex, or sexual orientation. We are an Athena SWAN Silver Award winner, a Disability Confident Leader and a Stonewall Diversity Champion. For technical issues when applying online please email support.jobs@imperial.ac.uk .
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