AI for Science Residency - Computational Chemistry
Core
Design, implement, and scale methods for generating chemically diverse molecular structures, including complexes, conformers, and reaction geometries, to fill gaps in chemical space for machine learning model training.
Role type
Research Scientist (Computational Chemistry / AI for Science)
Builds
Automated, reproducible data-generation workflows and datasets for training ML models in quantum chemistry.
Domain
Computational Chemistry / Quantum Chemistry / AI for Science
Deliverable
production ML models
Required skills
Electronic-structure methods (DFT, semiempirical/tight-binding), molecular structure generation and sampling, Python, GPU-accelerated computing, high-performance computing, open-source scientific software development, wavefunction-based methods (coupled cluster), thermochemistry benchmarks, integration with quantum-chemistry codes (ORCA, PySCF, CP2K)
Preferred skills
None stated
Technologies
Python, xTB/GFN, ORCA, PySCF, CP2K, GPU
Responsibilities
Design and implement structure-generation approaches (graph-based, fragment-based, molecular dynamics/metadynamics), build and maintain automated data-generation workflows, contribute to efficiency of data-generation pipelines, prepare technical papers and open-source releases
Seniority
PhD Researcher / Postdoc level