2025-12-04 –, Quest
CRISPR gene editing is transforming how we approach challenges in health, food, and sustainability, but one question still slows everyone down: which guideRNA will actually work?
A guide RNA is the molecule that tells the CRISPR system where to cut or modify DNA. Predicting how well it performs is essential for everything from developing new therapies to improving crops or designing cleaner bioprocesses.
For my MSc project, I used the Snellius supercomputer at SURF, supported through the EuroCC Netherlands infrastructure, to test whether adding RNA structure information could make prediction models smarter. Scaling this workflow on HPC let me process tens of thousands of sequences, train deep-learning models efficiently, and keep every step reproducible.
The work shows how advanced computing can bridge scientific insight and industrial impact, illustrating how reproducible, large-scale AI workflows can drive innovation across sectors that depend on complex biological or experimental data.
Sjoerd Kelder is a data scientist specializing in scaling machine learning workflows on high-performance computing systems. For his MSc in Information Studies (UvA, 2025) he used SURF’s Snellius HPC system to benchmark deep learning models with extended feature sets on large datasets. He now works with Fearless League on AI-powered lab tooling and reproducible ML infrastructure. His interests include workflow design, reproducibility practices, and efficient deployment of ML pipelines on large compute resources, applicable across sectors from science to industry.
University of Amsterdam (MSc Information Studies, 2025) / Fearless League (AI R&D)