Brian Groenke
Postdoctoral Researcher at PIK - Potsdam Institute for Climate Impact Research
Potsdam, Brandenburg, Germany
Summary
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Rockstar🎓
Top SchoolBrian Groenke is a postdoctoral researcher and software engineer with 13 years of experience applying programming, Bayesian methods, and scientific machine learning to Earth system and climate problems. Currently at the Potsdam Institute for Climate Impact Research, he focuses on differentiable programming for Earth system modeling after a PhD quantifying permafrost uncertainty using numerical and inverse modeling techniques. He combines hands-on software design and applied ML with a strong grounding in Bayesian inference, having applied these methods to stochastic weather generation and permafrost modeling. Based in Potsdam, he blends academic rigor with practical engineering from prior industry roles in software development and data science, and is known for thinking deeply across science, code, and philosophy.
13 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Colorado BoulderBachelor of Science (B.S.), Computer Science and Engineering, Bachelor of Science (B.S.), Computer Science and Engineering at The Ohio State University
English, German
Github Skills (200)
probabilistic-programming10
3d10
robotics10
html10
arctic10
julia10
scientific-machine-learning10
uwp10
dde10
java-game10
ode10
utf10
windows10
fluid-dynamics10
bayesian-inference10
Programming languages (14)
Github contributions (5)
NumericalEarth/Terrarium.jl
Aug 2025 - Jul 2026
A framework for building next-generation differentiable and GPU-accelerated land and ecosystem models in Julia.
Contributions:71 reviews, 30 PRs, 215 pushes in 11 months
gpujuliadifferentiable-programminghydrological-modelland-surface-model
bgroenks96/normalizing-flows
Jul 2019 - Jun 2021
Implementations of normalizing flows using python and tensorflow
Contributions:1 release, 73 commits, 2 PRs in 1 year 10 months
pythontensorflowmachine-learningmachine-learning-algorithmsnormalizing-flows
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