|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||£17,609 stipend|
|Placed On:||20th May 2022|
|Expires:||20th August 2022|
Applications are invited for a fully-funded Ph.D. studentship in the Department of Aeronautics. The scientific context is set by the EU-funded PhyCo project (https://cordis.europa.eu/project/id/949388) and UKRI ExCalibur project (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/W026686/1) with focus on the time-accurate prediction of chaotic and turbulent flows.
The studentship is for 3.5 years and will provide full coverage of tuition fees and an annual tax-free stipend of approximately £17,609 for EU and International students.
Further information about fee status can be found at https://www.imperial.ac.uk/students/fees-and-funding/tuition-fees/fee-status/.
The student will start their PhD in October 2022.
Closing date: until filled
The ability of fluid mechanics modelling to predict the evolution of a flow is enabled both by physical principles and empirical approaches. On the one hand, physical principles (for example conservation laws) are extrapolative – they provide predictions on phenomena that have not been observed. On the other hand, empirical modelling provides correlation functions within data. Artificial intelligence and machine learning are excellent at empirical modelling. The EU-funded PhyCo (https://cordis.europa.eu/project/id/949388) project will combine physical principles and empirical modelling into a unified approach: physics-constrained data-driven methods for multi-physics optimisation. A fully-funded PhD studentship is available to tackle the time-accurate prediction of chaotic and turbulent flows with a focus on quantum algorithms. The output of the project will be a model that learns the chaotic dynamics any time that data is assimilated without violating the physics. We will prototype the methods on low-dimensional ordinary differential equations, and we will scale up the method to higher-dimensional chaotic flows.
Applicants for a PhD should have a strong (first-class, or equivalent) academic track record in a scientific mathematical, or engineering discipline. Background in computational physics / mathematics and dynamical systems for fluid mechanics are an advantage. The post-holder will gain experience in physics-constrained machine learning; chaotic dynamical systems; computational methods for turbulent flows.
If you are interested in applying, initial informal enquiries can be made to the PhD supervisor Luca Magri, email@example.com. The application must be submitted on https://www.imperial.ac.uk/study/pg/apply/how-to-apply/apply-for-a-research-programme-/find-a-doctoral-course/.
For queries regarding the application process and admin, please contact Lisa Kelly at firstname.lastname@example.org.
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