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Field
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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
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molecular dynamics simulations. This position emphasizes research in the modeling of complex chemical systems, where the candidate will integrate advanced simulation techniques with modern machine learning
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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/TranSIESTA), or Molecular Dynamics (including hybrid QM/MM or ML-IP simulations) - Apply for computational resources in HPC facilities when needed. - Prepare periodic reports of the results and provide
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proposals. Grant reporting, managing and monitoring. Work with researchers, and industry partners to gather data and align simulations with practical needs. Identify gaps in existing bio-process models and
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information Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork Desired skills, knowledge and abilities: Experience with large-scale molecular dynamics (MD) simulations
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thereafter. The project Currently, plant ingredients are often refined to almost molecular purity - and then combined again to create structured foods. This isolation is resource intensive, and the removal
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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
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that requires accurate sub-grid models (e.g., Particle-in-Cell or Vlasov codes) coupled to a hydrodynamic simulation. In general, charged-particle transport is a non-trivial task, not only because of the large
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specializes in fluorescence and Raman-based approaches, integrating advanced microscopic analysis to gain molecular-level insights into complex materials and systems. The lab is internationally recognized