Technical Skills
My work sits at the intersection of physics, AI, and infrastructure. I enjoy combining rigorous experimentation with pragmatic engineering so that research teams can ship trustworthy results without losing speed.
Programming & Data Science
Python & PyData
100%8+ years
Daily driver for experimentation with NumPy, pandas, SciPy, Plotly, and friends.
PyTorch & JAX
85%6+ years
Research-grade neural and probabilistic models, including differentiable physics workflows.
Probabilistic modelling
80%5+ years
Bayesian calibration, density estimation, and uncertainty-aware ML for scientific data.
Scientific Computing & HPC
Slurm & Lmod stacks
85%5+ years
Scheduling, accounting, and user enablement for institute-scale clusters.
Containerised workflows
80%5+ years
Singularity/Apptainer, Conda, and CUDA images for portable research pipelines.
GPU & MPI workloads
70%4+ years
Profiling multi-GPU, MPI/OpenMP jobs and helping teams scale their experiments.
Reproducibility & Research Software
Git + testing
85%8+ years
Git, pytest, mypy, and packaging that link notebooks to published figures.
Documentation pipelines
75%7+ years
Sphinx/LaTeX/Markdown stacks so collaborators can follow every experimental step.
DevOps & Platforms
GitLab/GitHub automation
80%5+ years
CI/CD, container registries, and release workflows for research services.
Security & monitoring
70%4+ years
CVE tracking, dependency scanning, and lightweight observability for HPC services.