Oskar Weser
Research Software Engineer at European Centre for Medium-Range Weather Forecasts - ECMWF
Bonn, North Rhine-Westphalia, Germany
Summary
👤
Senior🎓
Top SchoolOskar Weser is a research software engineer with 10 years' experience bridging quantum chemistry and high-performance computing, now applying his expertise at ECMWF after a productive postdoc at MIT. He has driven large-scale, production-grade scientific software—accelerating electronic structure simulations up to 100× with GPU and sparse linear algebra work and scaling QMC codes across 10,000+ CPU cores. A proven open-source contributor, he improved core functionality in widely used projects like pymatgen and cclib, focusing on symmetry analysis, parser robustness, and maintainability. Oskar combines deep numerical and algorithmic skills with modern software practices (mypy, CI, testing) and a track record of mentoring researchers into better engineering habits. Based in Bonn, he’s motivated to translate advanced computational methods from academia into operational, impact-driven environments.
10 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Mathematik, Bachelor of Science - BS, Mathematik at FernUniversität in HagenMaster of Science - MS, Chemie, Master of Science - MS, Chemie at The University of Göttingen
Github Skills (14)
algorithm10
data-structures10
algorithms10
quantum-chemistry10
computational-chemistry10
science10
python10
data-structure10
materials-informatics10
numpy10
material-components9
material39
material-design9
scipy8
Programming languages (8)
Github contributions (5)
cclib/cclib
Oct 2017 - Jan 2018
Parsers and algorithms for computational chemistry logfiles
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 commits, 5 PRs, 24 comments in 3 months
Contributions summary:Oskar primarily focused on maintaining and improving the `cclib` library, which parses and provides algorithms for computational chemistry logfiles. Their contributions include ensuring PEP8 compatibility, changing the setup.py script, adding support for Molpro gradients, and improving comments within the code. These changes demonstrate an understanding of the project's codebase and a focus on code quality and maintainability within the context of computational chemistry data parsing.
computational-chemistrylogfileparserpythonquantum-chemistry
materialsproject/pymatgen
Aug 2017 - Aug 2017
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Role in this project:Back-end Developer
Back-end DeveloperContributions:27 commits, 2 PRs, 12 comments in 21 days
Contributions summary:Oskar primarily contributed to the `pymatgen` library by introducing and modifying methods related to symmetry analysis, specifically focusing on the `PointGroupAnalyzer` class. Their work includes the implementation of `cluster_sites` and `get_equivalent_atoms` methods, which are core functionalities for atom equivalence determination. The user also improved the `generate_full_symmops` functionality and fixed bugs in the test suite to ensure the correctness of their code. These changes centered on enhancing the library's capabilities in materials analysis, aligning with the project's core purpose.
pymatgenpythonsciencematerials-sciencematerials-informatics
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