Brian De Keijzer
Projectleider at Heijmans
Delft, South Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolBrian De Keijzer is a technical manager and computational physicist with a decade of experience applying high-performance computing, nonlinear PDE solvers, and AI to real-world engineering challenges. He has transitioned from publishing invited theoretical physics contributions and Nature Communications–linked HPC tooling to leading technical teams and industrial design work for energy and infrastructure clients. Brian blends hands-on numerical methods and compute-grant acquisition (€~180k total) with teaching and program management—having built quantum internships and coordinated applied-physics labs. Equally comfortable in academic collaborations across Europe and in industry-facing consulting, he’s known for turning complex simulations into practical roadmaps that accelerate partners like ASML. A former live-event technician, he brings calm operational leadership under pressure and a knack for pragmatic, cross-disciplinary problem solving.
10 years of coding experience
3 years of employment as a software developer
Master of Science Physics and Astronomy, Master of Science Physics and Astronomy at Vrije Universiteit Amsterdam (VU Amsterdam)Master of Science Physics and Astronomy, Master of Science Physics and Astronomy at University of AmsterdamBachelor of Science Applied Physics, Bachelor of Science Applied Physics at De Haagse Hogeschool / The Hague University of Applied SciencesHonors/Regents High School/Secondary Diploma Program, Honors/Regents High School/Secondary Diploma Program at Rietveld Lyceum
Dutch, English
Github Skills (91)
python10
deep-learning10
pde-solver10
gpu10
object-detection10
computer-vision10
semantic-segmentation10
spectral10
machine-learning10
pose-estimation10
pde9
object-tracking9
numpy9
onnx9
hardware-acceleration9
Programming languages (8)
Github contributions (5)
deKeijzer/Multivariate-time-series-models-in-Keras
Dec 2018 - Jan 2019
This repository contains a throughout explanation on how to create different deep learning models in Keras for multivariate (tabular) time series prediction.
Contributions:51 commits, 52 pushes, 1 branch in 1 month
deep-learningkerastime-series
deKeijzer/LaTeX-Rapportage-Template-TIS-TN
Apr 2018 - Oct 2019
Contributions:17 commits, 2 PRs, 12 pushes in 1 year 5 months
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