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Dr Omar Jamil

Senior Research Software Engineer

 Laver Building 

 

Laver Building, University of Exeter, North Park Road, Exeter, EX4 4QE, UK

Overview

Omar is a Senior Research Software Engineer in the RSE group. 

Career

Omar has an MPhys from University of Warwick and a PhD in Physics from University of Southampton. As part of his PhD and later for post-doc at Ohio University, Omar developed simulations for modelling the radiation signature from accreting black holes. These simulations included physical processes such as plasma internal shocks, synchrotron radiation, Compton scattering, and electron/positron pair creation and annihilation. The radiative transfer simulations made use of both distributions-based methods and Monte Carlo. As a post-doc Omar also co-supervised graduate students and taught courses.

Omar then joined the Met Office as a Scientific Software Engineer where he was responsible for maintaining and developing the user interface to the weather model. He also worked on atmospheric and ocean model coupling. He then joined the Defence Applications team as a Senior Scientist where he was responsible for developing and maintaining Tactical Decision Aids (used by the military) that forecast the impact of weather on Infra-red sensors used by the military. As part of the TDA development project Omar carried out field trials at a US military base and regularly handled highly sensitive data. He then joined the Clouds and Radiation Group at the Met Office where he initially worked on radiative transfer modelling with the main aim of implementing a line-by-line model.

Omar’s more recent focus has been on developing machine learning models to emulate physical processes. Using deep learning frameworks (PyTorch and Tensorflow), he has created emulators for thermodynamic processes of the atmosphere. Omar has also developed emulators using both random forests and neural networks for predicting thermospheric temperature used for space weather forecasting. Omar also helped establish the Data Science Community of Practice at the Met Office and as part of this and other machine learning work has made use of cloud computing resources, specifically AWS compute instances and Sagemaker.

Research

Teaching

Supervision / Group

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