- Reading time: 3 minutes
- Price: Free download
- Published: 17th September 2026
- Word count: 604 words
- File format: Text
Personal statement example
My interest in mathematical biology began with a second-year module on dynamical systems, where we spent two weeks on predator-prey models. What struck me was not the elegance of the phase portraits but how much the models left out, and how carefully you have to argue about which omissions matter. I wanted to know how modellers decide, and that question has shaped the rest of my degree.
For my final-year project I modelled the seasonal population dynamics of Daphnia in a small urban pond, using a compartmental system of ordinary differential equations with temperature-dependent birth rates. I used publicly available monthly water temperature data from a nearby monitoring site as a forcing term, and fitted two parameters by least squares to sampling counts collected by a local wildlife group. The fit was mediocre, which turned out to be the interesting part. Adding a simple delay to reflect maturation time improved the seasonal timing considerably, but also made the system sensitive to initial conditions in ways I could not fully characterise with the tools I had. Writing that up honestly, rather than presenting the tidier version, taught me more than the modelling itself. I earned a strong first for the project and my supervisor encouraged me to pursue the subject further.
Alongside the project I took modules in stochastic processes, statistical inference and numerical analysis, and I chose a computational statistics option specifically to strengthen my R. I now work comfortably with differential equation solvers in R and can manage moderate data-wrangling tasks in Python, though I would describe myself as a competent user rather than a strong programmer. I have read parts of Murray's Mathematical Biology and found the chapters on reaction-diffusion patterning genuinely absorbing, particularly the way a mechanism that is mathematically simple can produce such varied structure. I would like to study stochastic and spatial models more systematically, since my project convinced me that deterministic ODEs are a limited description of small populations where chance events dominate.
Outside my degree I work weekend shifts at the information desk of a city aquarium. Explaining why a tank's jellyfish population rises and falls, to a family with three impatient children, is not a research skill, but it has made me much better at judging what an audience already knows and what I can safely leave out. I have found the same judgement useful in seminar presentations. I am also treasurer of a local running club and help coach the beginners' group. Keeping the accounts for around ninety members, reconciling race entry fees and presenting a clear annual statement to the committee has made me methodical about record-keeping, and I now document my code and data sources far more carefully than I did in my second year. Coaching has taught me patience with people who improve slowly and unevenly, which is closer to how I experience mathematics than I once expected.
I am drawn to taught postgraduate study rather than immediate employment because I want the mathematical grounding to work on biological problems properly, rather than applying whichever method I happen to know. I am especially keen to develop skills in stochastic simulation, parameter inference for mechanistic models and spatial modelling, and to work on a dissertation involving real data and real uncertainty. In the longer term I would like to work as part of a research group on ecological or epidemiological modelling, and possibly to continue to doctoral study if my dissertation goes well. I know the mathematics will be demanding, but my project showed me that I enjoy the slow, uncomfortable stage where a model does not yet work.
Why this example works — strengths and ways to improve
This is a strong, coherent taught master's statement. You build motivation from a genuine intellectual question about model omissions, support it with a well-described final-year project, and reflect honestly on its limits. Your self-assessment of programming is credible. The main weaknesses are a slightly prestige-leaning project sentence and a closing that could connect future aims more concretely to the methods you name.
Subject motivation
how carefully you have to argue about which omissions matter
Your motivation rests on a precise question rather than general fascination. Focusing on what models leave out signals mature thinking about modelling as argument. Because you say this question shaped your degree, the later project and module choices read as consequences rather than a list.
Academic preparation
modules in stochastic processes, statistical inference and numerical analysis
These modules map directly onto the stochastic, inferential and computational skills you later say you want to develop. Choosing computational statistics to strengthen R shows deliberate preparation. You could briefly name one stochastic concept you handled, so readers can gauge your current depth before advanced study.
Evidence and reflection
The fit was mediocre, which turned out to be the interesting part.
This is your best evidence. You report the method, data sources, fitting approach and a specific modification, then reflect on what remained unresolved. Admitting that you could not fully characterise sensitivity to initial conditions shows research honesty. It also justifies your stated move towards stochastic and spatial models.
Relevant experience
I now document my code and data sources far more carefully
Your aquarium and treasurer roles are ordinary but well used, because each is tied to a concrete habit: judging what an audience knows and keeping careful records. The coaching link to uneven progress is more tenuous. It reads as a reflective aside rather than evidence of readiness.
Credibility and voice
a competent user rather than a strong programmer
Your measured self-description builds trust, and the voice is consistently personal and unforced. The line about earning a strong first and your supervisor's encouragement is the one moment that leans on outside validation. Your reflection already demonstrates quality, so that sentence adds little.
Structure and format
I enjoy the slow, uncomfortable stage where a model does not yet work
Your paragraphs move logically from motivation to project, preparation, experience and aims, and the prose is economical. The ending returns well to your project rather than offering a generic flourish. Before submitting, tailor the aims paragraph to the actual programme and prompt.
What you’ve done well
- Your final-year project is described with specific methods, data and limitations, and the reflection on an imperfect fit shows genuine research thinking.
- Your skills are calibrated honestly, especially on programming, which makes your other claims more believable.
- Your stated interest in stochastic and spatial modelling grows directly from a limitation you found in your own work, which gives the statement strong internal coherence.
How this draft could improve
- Replace or shorten the sentence about your grade and your supervisor's encouragement, and let your reflection on the delay model carry the weight.
- Add one concrete example of a stochastic or inferential idea you have already worked with, to show where your preparation currently stands.
- Make your future aims slightly more specific, for example by naming the kind of ecological or epidemiological question you would want a dissertation to address.