Martin Grignard
Tech Lead at Haulogy
Liège, Liège, Belgium
Summary
👤
Senior🎓
Top SchoolMartin Grignard is a Tech Lead and versatile engineer with eight years of experience building reproducible pipelines, high-performance computing platforms, and large-scale storage systems for scientific research in bioinformatics and neuroimaging. He combines hands-on Python and Go development with infrastructure automation (Docker, Nextflow, Singularity, Slurm) to deliver maintainable, CI/CD-driven solutions for multi-disciplinary teams. At the University of Liège he co-managed a 14PB StorNext system and heterogeneous HPC cluster, improving throughput and reproducibility through custom tooling and automation. An active open-source contributor, he enhanced SALib’s truncated normal support and testing, reflecting a pragmatic focus on robustness and clarity. Trained as a PhD engineer and former lecturer, he pairs deep technical breadth with teaching and client-facing delivery experience, seeking leadership roles where research-grade engineering meets production impact.
8 years of coding experience
3 years of employment as a software developer
Ph.D. Engineering sciences and technologies, Ph.D. Engineering sciences and technologies at University of LiègeMaster 2 (M2) Polyvalent industrial engineering science (Electronics), Master 2 (M2) Polyvalent industrial engineering science (Electronics) at HELMo GrammeIllustration, Illustration at ESA Saint-Luc Liège
English, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
numpy10
python10
pytest9
testing9
Programming languages (9)
Github contributions (5)
SALib/SALib
Nov 2020 - Nov 2020
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 1 PR, 2 comments in 1 day
Contributions summary:Martin primarily contributed to the `SALib` library by adding and refining functionalities related to the truncated normal distribution. Their work included implementing the distribution within the scaling methods and fixing related parameters and tests. The user also updated error messages for improved clarity and corrected typos, enhancing the library's robustness and usability. Additionally, they added a new test case for the truncated normal distribution functionality.
pythonsensitivity-analysis-librarysensitivity-analysissobolmorris
CyclotronResearchCentre/shamo
Dec 2019 - Apr 2022
A tool for electromagnetic modelling of the head and sensitivity analysis.
Contributions:7 releases, 361 commits, 59 PRs in 2 years 4 months
electromagneticpythonsensitivity-analysisheadsensitivity
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