Andy's profile pic!

Hi, and thank you for taking the time to visit my site. I am a Research Scientist/ Software Engineer/ Data Scientist working on:

  • algorithm implementation;
  • numerical simulation development;
  • high performance computing (HPC) clusters;
  • data management and processing;
  • numerical modelling of physical systems.

My work is currently applied to investigation of the processes behind the generation of the Earth’s magnetic field.

I have experience in Python; SQL; Fortran; bash; parallel programming on High Performance Computing (HPC) clusters; management of software projects and collaboration using GitHub; and automatic code documentation and testing using CI/CD tools such as GitHub Actions.

I am interested in Computers old and new, numerical algorithms and simulations, electronics and soldering, and generally anything related to technology. A couple of projects I have worked on are presented below.


Professional Certifications

  • Microsoft Certified: Azure Data Scientist Associate

    Awarded after passing proctored examination DP-100. The examination tested my proficiency and knowledge of designing and preparing machine learning solutions, exploration of data and running experiments on Azure cloud, training and deploying models, and optimisation of large language models for AI applications.

  • IBM Data Science Professional Certificate (V3)

    Practical and hands-on project based course on multiple aspects of data science and machine learning. Studied and completed projects with common data science libraries such as SciPy, Pandas, and NumPy while managing data with SQL and relational databases. Application of data and statistical analysis techniques by constructing machine learning models, presentation of results using Jupyter notebooks and online dashboards.

  • Microsoft Certified: Azure Data Fundamentals

    Awarded after passing proctored examination DP-900. This certification tested knowledge and proficiency in: core data concepts; relational and non-relational data on Azure; cloud storage capabilities such as data lakes, warehouses, and lakehouses; large scale analytics services such as Databricks and Microsoft Fabric; real-time and batch processing; ETL and ELT pipelines in Azure; and business information tools such as Microsoft Power BI.

  • Microsoft Certified: Azure AI Fundamentals

    Awarded for passing proctored examination AI-900. This certification tests knowledge and proficiency in: Artificial Intelligence (AI) workloads and considerations; machine learning on Azure; computer vision and Natural Language Processing; generative AI; and responsible AI principles.

Academic Publications

Here is a brief list of my scientific publications, links to my arXiv and ORCID pages are included for a more comprehensive list.

  • Andrew T. Clarke, Christopher J. Davies, Souvik Naskar, Stephen J. Mason, 2026. Accessing the dipole-multipole transition in rapidly rotating spherical shell dynamos. Geophysical Journal International:10.1093, https://doi.org/10.1093/gji/ggag092

  • Biggin, A.J., Davies, C.J., Mound, J.E., Lloyd, S.J., Engbers, Y.E., Thallner, D., Clarke, A.T. and Bono, R.K., 2026. Mantle heterogeneity influenced Earth’s ancient magnetic field. Nature Geoscience, pp.1-8. https://doi.org/10.1038/s41561-025-01910-1

  • Clarke, A., Davies, C., Ruprecht, D. et al., 2020. Performance of parallel-in-time integration for Rayleigh Bénard convection. Comput. Visual Sci. 23, 10. https://doi.org/10.1007/s00791-020-00332-3

  • Andrew T. Clarke, Christopher J. Davies, Daniel Ruprecht, Steven M. Tobias, 2020. Parallel-in-time integration of kinematic dynamos. Journal of Computational Physics: X, 7, 2590-0552. https://doi.org/10.1016/j.jcpx.2020.100057

google scholar: https://scholar.google.com

arXiv: https://arxiv.org

ORCiD: https://orcid.org

PhD Thesis

I completed my PhD on Parallel-in-time algorithms for Magnetohydrodynamic fluid flows at the University of Leeds. My PhD thesis,

  • Clarke, Andrew Thomas (2021) Parallel-in-time integration of astro- and geo- physical flows; application of Parareal to kinematic dynamos and Rayleigh-Bénard convection. Integrated PhD and Master thesis, University of Leeds.

Available at the White Rose eThesis repository, can be downloaded from this link:

Spherical Shell Convection

The molten iron in the Earth’s outer core undergoes turbulent convection. Much of what we know about physical systems such as the Earth’s core comes from computer simulations. To understand more about the processes happening under our planet’s surface, we can numerically solve the equations governing fluid flow in Earth-like geometry.

The video above shows convection in an Earth-like system, but at much less extreme conditions, since it is currently numerically impossible to simulate the full Earth-system. Rayleigh Number, which determines how much thermal driving is in the system: 2x10⁷, Ekman Number, which shows how fast the Earth is spinning (smaller means faster): 1.3x10⁻⁴, the magnetic Prandtl number Pm is 11, while the thermal Prandtl number Pr is 1. Solved using 120 points in radius, 144 points in co-latitude, and 288 points in azimuth.

Fluid Dynamics

The Navier-Stokes equations describe the way that fluids behave. In all but the most simple of cases, analytical solutions are impossible, and numerical solutions are required. One particularly well studied and interesting problem is that of Rayleigh-Bénard convection (RBC). RBC describes the buoyancy driven flow of a fluid heated from below and cooled from above. It can be uses as a simplified analogue for understanding a range of astrophysical and geophysical systems, such as the Sun, stars, planetary interiors and the atmosphere.

The above video shows Boussinesq Rayleigh-Bénard convection with fixed flux boundary conditions at a rather high Rayleigh number (5e10). The equations were solved using pseudospectral spatial discretisation using Fourier series in the horizontal direction and Chebyshev polynomials in the vertical. Time stepping was achieved using a Runge-Kutta IMplicit-EXplicit (IMEX) scheme of 3rd order (RK443). The code was written in python for the open-source spectral solver Dedalus.

GameBoy game

I wrote a GameBoy game! Here is a quick teaser video of “SUPER SHOOTER”:

I started this project to satisfy my curiosity in three main areas: old 8-bit computers, experience in C-programming language, and a nostalgia hit for the original GameBoy. The code was developed using the GameBoy Development Kit: gbdk, with testing and debugging taken care of by the bgb emulator. I was able to capture footage using the mGBA Emulator. I aim in future to port this game/write a new game in the updated GBDK-2020.