Ryan Jadrich

Metropolis Postdoctoral Fellow at University of Texas at Austin

Austin, Texas, United States
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Summary

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Senior
🎓
Top School
Ryan Jadrich is a Metropolis Postdoctoral Fellow and statistical model engineer with a decade of experience translating advanced Bayesian inference, Monte Carlo simulation, and optimization methods into experimentally realizable particle interactions and scalable data tools. He combines a Ph.D. in Chemistry with hands-on high-performance computing—developing Relative Entropy codes integrated with GROMACS and custom Monte Carlo simulators on supercomputers—to bridge theoretical models and laboratory implementation. Beyond academia he completed competitive Data Incubator and S2DS fellowships, building a Twitter-based disaster detection app and an NLP/regulatory-compliance prototype that processed decades of EU legislation. Known for recasting non-linear coexistence problems as convex optimization and for pragmatic software that enables experimentalists to replicate complex many-body behaviors, he operates at the intersection of statistical physics, machine learning, and production-ready data engineering. Based in Austin, TX, he brings a rare blend of theoretical rigor and applied systems experience that accelerates both scientific discovery and real-world productization.
code10 years of coding experience
job5 years of employment as a software developer
bookBachelors of Science (B.S.), Highest Honors, Chemistry, Minor in Mathematics, Bachelors of Science (B.S.), Highest Honors, Chemistry, Minor in Mathematics at Rochester Institute of Technology
bookData Science, Machine Learning, Data Science, Machine Learning at General Assembly
bookUniversity of Illinois Urbana-Champaign
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Github Skills (6)

dft3
polymer3
interaction3
prism3
thermodynamics3
materials-informatics2

Programming languages (1)

Python

Github contributions (5)

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Contributions:119 commits, 116 pushes, 2 branches in 8 months
Contributions:11 pushes, 2 branches in 1 year 8 months
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Ryan Jadrich - Metropolis Postdoctoral Fellow at University of Texas at Austin