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Building, modelling & occasionally breaking things in Python
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Building, modelling & occasionally breaking things in Python

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Robert-Study/README.md

Robert Gardner

I'm a physics graduate from the University of Birmingham, with a particular interest in scientific computing and experimental data analysis. My projects involve building models, fitting measurements and checking where the results agree with theory—and where they do not.

Most of the work here began during my degree. The repositories have since been developed into runnable examples with tests and clearer documentation.

Selected projects

I led the group project and worked mainly on the rotating-compensator ellipsometry analysis: extracting Ψ and Δ, calibrating the instrument and estimating thin-film properties.

The silicon-reference analysis gave a thickness of 55 ± 7 nm, compared with the certified 53.30 nm. Comparing gold-film measurements with profilometry revealed a strong correlation and a systematic difference between the two thickness scales.

71% group mark · 75% individual mark. The repository links to the report and includes an explanation of my contribution and a runnable synthetic example of the analysis pipeline.

A stochastic cellular automaton exploring how tree growth and lightning produce population oscillations and broad fire-size distributions. I extended the coursework with larger grids, parameter sweeps and analysis of fire events and cluster geometry.

The report describes runs reaching 16,000 × 16,000 cells. A smaller seeded example is included so the simulation can be reproduced on an ordinary computer.

74% on the original project. Try the interactive simulation.

Computational physics coursework covering finite quantum wells, Fourier analysis and rocket control. The public examples include a bound-state solver checked against an independent finite-difference calculation, and a synthetic signal-filtering demonstration.

Assessed work Mark
Quantum systems 95%
Self-landing rockets 90%
Spectral analysis 80%
Programming worksheets 98% average

Experimental work

Other work

Binary Search Tree Optimisation studies how a distribution over tree roots changes the expected reward for each key. The current analysis uses linear programming to examine the trade-off between average reward and the worst outcome.

Main tools: Python, NumPy, SciPy, Matplotlib, pandas, Streamlit, Git and LaTeX.

home@robertgardner.co.uk

Pinned Loading

  1. Forest-Fire-Analysis Forest-Fire-Analysis Public

    Optimised Python forest-fire simulation exploring self-organised criticality, oscillatory dynamics, scaling laws and heavy-tailed fire-size distributions.

    Python 1

  2. Rotating-Compensator-Ellipsometry Rotating-Compensator-Ellipsometry Public

    Python pipeline for rotating-compensator ellipsometry, including calibration, harmonic fitting, Fresnel modelling and thin-film parameter extraction.

    Python 1

  3. Scientific-Python-Projects Scientific-Python-Projects Public

    Scientific Python projects in quantum systems, Fourier analysis and autonomous rocket simulation, demonstrating numerical modelling and computational physics.

    Python 1

  4. Binary-Search-Tree-Optimisation Binary-Search-Tree-Optimisation Public

    Exploration of reward optimisation in depth-based binary search trees using weighting, rounding and numerical analysis.

    Python 1