-
researchers will work in a dynamic team of staff scientists at Argonne National Laboratory. Within the team we have extensive experience with large scale molecular dynamics simulations, first principles
-
the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
-
at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
-
contributions in: Building novel generative models for predicting genome-scale evolutionary patterns using GenSLMs Developing scalable models that can, when integrated with high throughput molecular dynamics
-
Experience Experience with biomolecular Molecular Dynamics Simulations. Familiarity with Machine Learning / Artificial Intelligence. Familiarity with standard data science and machine learning python libraries