Skills
My PhD work sits at the intersection of statistics, simulation, and software — the same core toolkit used in quantitative research, data science, and software engineering roles.
Quantitative & Statistical Modelling
- Probabilistic modelling, Bayesian inference, hidden Markov models, and transition matrix methods.
- ODE-based and stochastic simulation, with uncertainty quantification and sensitivity/scenario analysis.
- Model back-testing, validation, and parameter estimation against empirical data.
- Synthetic dataset generation and time series analysis.
- Machine learning for surrogate modelling and training data generation.
Software Engineering & Data Science
- Modular, production-quality Python, R, and Bash for building analysis pipelines and reproducible research workflows.
- Version control with Git, including teaching it to undergraduate and master’s students.
- HPC deployment — cluster scheduling and array jobs — for large-scale parameter sweeps.
- Collaborative research workflows, and processing/validating large-scale, high-dimensional datasets.
Communication & Writing
- Peer-reviewed publication and invited/award-winning talks (Best Talk, QMUL Third Year Symposium; invited speaker, LSHTM).
- 200+ hours teaching technical material to diverse audiences.
- Translating complex quantitative methods for non-technical stakeholders.
Python, R, Bash, Git, Linux, HPC, Jupyter, LaTeX
See my Research and CV pages for where these skills have been applied.
Janeesh Kaur Bansal