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<div class="elementToProof"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Harnessing Causally-Informed Deep Learning for Use in Climate Modelling </span></div>
<div class="elementToProof"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><a href="https://jobs.reading.ac.uk/Job/JobDetail?JobId=13080" id="OWAaa92b47e-f1f1-ff90-8ffe-1208af9722c9" class="OWAAutoLink" data-loopstyle="linkonly" style="margin: 0px;">https://jobs.reading.ac.uk/Job/JobDetail?JobId=13080</a></span></div>
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Supervisor: Dr Todd Jones, t.r.jones@reading.ac.uk</div>
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<div class="elementToProof"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">4-Year PhD: 25% time as Teaching Assistant, Department of Computer Science, University of Reading, UK</span></div>
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Salary: £6,994-8,245 per annum + maintenance stipend £14,428 per annum</span></div>
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Requires: degree in Computer Science, Mathematical Science, Meteorology, Physics or closely related subjects</span></div>
<div style="text-align: left; text-indent: 0px; margin: 0px;"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Closing date: 9 April 2024</span></div>
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Interviews expected the week commencing 22nd April 2024.</span></div>
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<div class="elementToProof"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">This project aims to develop a novel deep learning framework that leverages the power of causality to emulate complex atmospheric processes.
This involves:</span></div>
<div><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">- Generating and utilizing high-fidelity training data from Large Eddy Simulation (LES) models.</span></div>
<div class="elementToProof"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">- Enhancing model performance and computational efficiency, with a focus on enhancing GPU functionality in an HPC environment.</span></div>
<div><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">- Applying your deep learning model within a superparameterization framework to improve largescale climate projections.</span></div>
<div class="elementToProof"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">By integrating causally-informed deep learning with LES model training data, we seek to emulate high-resolution atmospheric processes
at a fraction of the computational cost. This approach not only enhances model performance via the potential to generate accurate high-resolution simulation data quickly, but it also offers a scalable solution to improve reliable climate projections, particularly
in models that superparameterize subgrid with LES. That is, climate models must represent small scale phenomena which their coarse spatial grids cannot explicitly resolve with necessary approximations. Historically, this is done via formulaic mathematical
functions as parameterizations. Some newer models instead represent these processes by embedding small area high-resolution LES models within the coarse grid to gain physical accuracy at exceptional cost. By emulating the LES, we intend to capture the physical
gains of high-resolution models at a fraction of the computational cost, a potentially pioneering step towards more accurate and computationally feasible climate modelling.</span></div>
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<div><span style="font-family: "Trebuchet MS", sans-serif; font-size: 13.3333px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">_________________________________</span></div>
<div style="margin: 0px; font-size: 14px;"><span style="font-family: "Trebuchet MS", sans-serif; font-size: 13.3333px; color: rgb(0, 0, 0);">Dr Todd R Jones, BSc MSc PhD AFHEA</span></div>
<div style="margin: 0px; font-size: 14px;"><span style="font-family: "Trebuchet MS", sans-serif; font-size: 13.3333px; color: rgb(0, 0, 0);">Lecturer in Computer Science</span></div>
<div style="margin: 0px; font-size: 14px;"><span style="font-family: "Trebuchet MS", sans-serif; font-size: 13.3333px; color: rgb(0, 0, 0);">University of Reading</span></div>
<div style="margin: 0px; font-size: 14px;"><span style="font-family: "Trebuchet MS", sans-serif; font-size: 13.3333px; color: rgb(0, 0, 0);">Polly Vacher Building, Room 149</span></div>
<span style="font-family: "Trebuchet MS", sans-serif; font-size: 13.3333px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">+44 (0)118 378 3187</span>
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