Pierre-paul De Breuck
Postdoctoral Researcher at Ruhr University Bochum
Bochum, North Rhine-Westphalia, Germany
Summary
👤
Senior🎓
Top SchoolPierre-paul De Breuck is a computational materials scientist and postdoctoral researcher with a decade of experience applying generative models and AI to energy materials and crystal structure prediction. Based at Ruhr University Bochum and collaborating with Mila alumni networks, he has built and benchmarked ML pipelines, curated crystal structure databases, and experimented with GFlowNets for crystal generation under Yoshua Bengio’s group. His background combines top-tier academic training (summa/magna cum laude) with internships at MIT and a PhD fellowship focused on active learning strategies to accelerate experiments. Known for bridging domain knowledge and machine learning, he brings both practical implementation experience and leadership from running a researchers’ association—an asset for interdisciplinary teams tackling materials discovery.
10 years of coding experience
High School Diploma, Mathematics and Science, High School Diploma, Mathematics and Science at College Paters Jozefieten MelleMaster's degree, Engineering Physics/Applied Physics, Summa Cum Laude, Master's degree, Engineering Physics/Applied Physics, Summa Cum Laude at Universit�� catholique de Louvain
French, Dutch, English
Github Skills (17)
dft10
materials-informatics9
machine-learning9
deep-learning8
prediction6
data-repository6
science5
physics5
chemistry5
benchmarking5
benchmark5
data-driven4
machine-learning-algorithms4
regression3
big-data-analytics3
Programming languages (3)
Github contributions (5)
ppdebreuck/SnO2_DFT-simulation
Apr 2018 - Jun 2020
Contributions:1 push in 2 years 3 months
ppdebreuck/modnet-matbench
Mar 2021 - Feb 2022
Data repository accompanying "Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet"
Contributions:11 pushes in 10 months
data-drivenbenchmarkingdata-repositoryscienceimbalance
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