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
95%7+ years
Daily driver for experimentation with NumPy, pandas, SciPy, Plotly, and friends.
PyTorch
75%4+ years
Research-grade models, including differentiable physics workflows.
Probabilistic modelling
25%1+ years
Bayesian inference, density estimation, and uncertainty-aware ML for scientific data.
Python Ecosystem & Packaging
Isolated environments
95%7+ years
venv, Conda, uv, and Poetry to keep research stacks reproducible across machines.
Packaging & publishing
75%5+ years
PEP 517 builds,linting (ruff) ,wheels, versioning, and PyPI/internal releases with automated checks.
Scientific Computing & HPC
Containerised workflows
95%4+ years
Docker/Podman, Singularity/Apptainer, buildx caching, and registry hygiene for portable research pipelines.
GPU workflows
75%4+ years
CUDA images, multi-GPU scheduling
Slurm & Lmod stacks
85%4+ years
Scheduling, accounting, and user enablement for institute-scale clusters.
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/Markdown workflows with consistent templating so collaborators can follow every experimental step.
Scientific writing (LaTeX)
70%7+ years
Scientific manuscripts and thesis with LaTeX classes, BibTeX/Biblatex, Zotero/Citavi
DevOps & Platforms
Security & monitoring
70%1+ years
CVE tracking, dependency scanning, and lightweight observability for HPC services.
Cybersecurity & hardening
70%2+ years
CVE triage for package stacks, kernel/userspace hardening baselines, and coordinating mitigations.
GitLab/GitHub automation
75%5+ years
CI/CD, container registries, and release workflows for research services.