Scientific computing toolkit
NumPy, SciPy, Matplotlib, Pandas, PyTorch, MATLAB, and LaTeX for reproducible modelling, simulation, plotting, and reports.
Learning Route
A compact map of modules, self-study, summer-school work, and public outputs.
Formal modules, self-study, and summer-school work are arranged as one academic trajectory.
NumPy, SciPy, Matplotlib, Pandas, PyTorch, MATLAB, and LaTeX for reproducible modelling, simulation, plotting, and reports.
Numerical solutions to the one-dimensional Schrodinger equation, quantum oscillator models, and Qiskit summer-school learning.
Gray-Scott reaction-diffusion, tumour-growth modelling, stability analysis, numerical validation, and performance-aware implementation.
Year 1
Analysis, algebra, methods, and first-principles physics.
Year 2
Complex analysis, fluids, electromagnetism, quantum, and statistical physics.
Year 3
Numerical methods, quantum mechanics, stochastic methods, GR, and solid-state physics.
The emphasis stays on trajectory; the exact modules remain visible for reference.
Year 1
This stage led into the Lagrangian mechanics summer project report.
Open detailed course noteYear 2
This layer connects to PDE modelling, stability analysis, and numerical solutions.
Open detailed course noteYear 3
Current work on Schrodinger models, Gray-Scott simulation, Qiskit, and scientific Python supports this direction.
Open detailed course noteProjects and reports are collected under Projects & Outputs. Shorter learning notes, reading reflections, and technical write-ups stay under Notes & Writing.
Portfolio assistant