Christian Forssén
Professor, Head Of Division Subatomic, High-Energy And Plasma Physics
Greater Gothenburg Metropolitan Area Sweden
Summary
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Senior🎓
Top SchoolChristian Forssén is a professor and Head of Division for Subatomic, High-Energy and Plasma Physics at Chalmers University of Technology, bringing over a decade of academic leadership and deep expertise in theoretical nuclear and particle physics. He combines rigorous theoretical work in low-energy QCD with advanced computational methods—high-performance computing, Bayesian statistics and machine learning—to tackle foundational problems and produce cross-disciplinary publications in venues like Nature Physics and PRL. A prolific mentor and educator, he has supervised over 100 bachelor and master projects and routinely teaches both core physics and applied data-analysis courses. His career includes international research stints at Lawrence Livermore, Oak Ridge, and the University of Tennessee, reflecting a rare mix of national-laboratory experience and university leadership. Less obvious is his track record of bridging applied mathematics and ML with nuclear theory, enabling novel computational approaches that inform both basic science and methodological development.
10 years of coding experience
12 years of employment as a software developer
PhD, Physics, PhD, Physics at Chalmers University of Technology
Swedish, English, German, French
Github Skills (14)
machine-learning10
python10
data-science10
support-vector-machine9
logistic-regression9
unsupervised-learning9
algorithms8
decision-tree8
bayesian-methods8
random-forest8
html7
deep-learning6
data-analysis6
neural-network5
Programming languages (1)
Github contributions (5)
cforssen/LearningFromData-content
Oct 2023 - Aug 2025
The contents (markdown, notebooks, scripts) for the Learning from data JupyterBook
Contributions:3 reviews, 3 PRs, 59 pushes in 1 year 11 months
physics-chalmers/tif285
Aug 2019 - Oct 2022
Learning from data
Contributions:2 reviews, 361 commits, 5 PRs in 3 years 2 months
pythonmachine-learningdata-science
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