Rolf David
Junior Professor Chair at CNRS
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolRolf David is a computational chemist and Junior Professor whose decade-long career bridges theoretical chemistry, machine learning and advanced simulation methods to unravel reaction mechanisms at interfaces and in solution. He combines expertise in DFT, QM/MM, MD, enhanced sampling and transition path sampling with neural-network potentials to study reactivity, spectroscopy (IR, vSFG) and solvent/ion effects, translating cutting‑edge methods into production code and ML tools. His postdoctoral work developed AI-designed reaction coordinates and integrated NNP+TPS workflows to probe phosphoester and peptide bond formation across bulk and interfacial environments. Comfortable on national HPC systems and in mentoring students, he has a strong track record of securing large compute allocations and building analysis/spectroscopy software—an uncommon blend of deep theory, software engineering and practical high-performance computing.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Theoretical Chemistry, Doctor of Philosophy - PhD, Theoretical Chemistry at Université Grenoble AlpesMaster's degree, 1st year (Maîtrise), Organic Chemistry, Master's degree, 1st year (Maîtrise), Organic Chemistry at Université de Caen Basse NormandieMaster's degree, Organic Synthesis, With distinctions, Master's degree, Organic Synthesis, With distinctions at Université Joseph Fourier (Grenoble I)
French, English, German
Github Skills (16)
ase7
lammps6
paddle6
nodejs6
computational-chemistry6
cpp6
machine-learning6
sourceforge6
cuda5
python5
subversion5
pytorch5
deep-learning4
tensorflow4
git-mirror3
Programming languages (2)
Github contributions (5)
cdavro/sem_forces
Aug 2016 - Mar 2021
Contributions:45 commits, 42 pushes, 3 branches in 4 years 7 months
arcann-chem/arcann_training
May 2024 - Jun 2026
An automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
Contributions:9 reviews, 32 PRs, 59 pushes in 2 years
machine-learning
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