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Learning Route

From mathematics and physics coursework to computational research habits

A compact map of modules, self-study, summer-school work, and public outputs.

Learning route

Formal modules, self-study, and summer-school work are arranged as one academic trajectory.

Scientific computing toolkit

NumPy, SciPy, Matplotlib, Pandas, PyTorch, MATLAB, and LaTeX for reproducible modelling, simulation, plotting, and reports.

Computational quantum mechanics

Numerical solutions to the one-dimensional Schrodinger equation, quantum oscillator models, and Qiskit summer-school learning.

PDEs, modelling, and HPC

Gray-Scott reaction-diffusion, tumour-growth modelling, stability analysis, numerical validation, and performance-aware implementation.

Year 1

Foundations

Analysis, algebra, methods, and first-principles physics.

  • calculus
  • linear algebra
  • classical mechanics
  • waves
  • thermal physics

Year 2

Methods and core physics

Complex analysis, fluids, electromagnetism, quantum, and statistical physics.

  • complex analysis
  • fluid mechanics
  • electromagnetism
  • quantum physics
  • statistical physics

Year 3

Advanced modelling and computation

Numerical methods, quantum mechanics, stochastic methods, GR, and solid-state physics.

  • numerical methods
  • quantum mechanics
  • general relativity
  • stochastic methods
  • solid state physics

Course map without losing the story

The emphasis stays on trajectory; the exact modules remain visible for reference.

Year 3

Advanced modelling and computation

Current work on Schrodinger models, Gray-Scott simulation, Qiskit, and scientific Python supports this direction.

Open detailed course note
Exact modules
Planned auditing
  • Mathematics for Quantum Mechanics, Level 7
  • Mathematics for AI and Machine Learning, Level 6
  • Frontier Mathematics Computing, Level 7

Where to go next

Projects and reports are collected under Projects & Outputs. Shorter learning notes, reading reflections, and technical write-ups stay under Notes & Writing.