Saul Harwin

BSc Physics graduate passionate about software engineering, driven by curiosity and innovative problem solving.

About Me

Saul Harwin

I'm a recent Physics graduate (2:1 BSc) with a solid foundation in analytical thinking, problem-solving, and a growing passion for software engineering. Throughout university, I developed my technical and programming skills through both coursework and self-directed projects, and I’m now excited to apply that knowledge in real-world development environments. Studying Physics taught me how to approach complex problems methodically and think critically — skills that translate naturally into writing clean, efficient code. Beyond academics, I’ve volunteered at a local sailing club, teaching children and coordinating activities. This experience strengthened my communication, leadership, and collaboration abilities — qualities I bring to any team setting. I'm actively seeking junior software engineering opportunities where I can learn from experienced developers, contribute to meaningful projects, and grow my career at the intersection of science and technology. If you're working on something exciting in that space, please get in touch.

Projects

Python

Matplotlib

Numpy

Data Analysis

Error Analysis

Data Visualisation

Model Fitting

Weak Gravitational Lensing

For my final year project at the University of Sussex, I conducted an in-depth analysis of a synthetic galaxy cluster through weak gravitational lensing. This involved modeling lensing effects using the Singular Isothermal Sphere (SIS) model and estimating key astrophysical parameters such as the Einstein radius, velocity dispersion, and mass.

R

Monte Carlo Methods

Numerical Methods

Statistical Computing

Data Visualisation

Markov Chain

Stochastic Processes

Probability Theory

Python

Monte Carlo Dissertation

In my dissertation for my Monte Carlo Simulations module in 3rd year, I explored the application of Monte Carlo simulation methods to solve problems in probability theory, stochastic processes, and numerical analysis, with practical applications like probability distribution sampling, Markov Chains, and solving differential equations.

C#

Unity

Flocking Bird Simulation

Developed a Unity simulation using the Boids algorithm to model realistic flocking behavior. Implemented rules for separation, alignment, and cohesion to control agent movement. Tuned parameters for smooth, lifelike group dynamics. Gained experience with vector math, Unity physics, and real-time rendering.