Research Associate, Atomistic Simulations & AI-Driven Molecular Modeling

Oak Ridge National Laboratory•Oak Ridge, TN

73d•Onsite

About The Position

The Multiscale Biomedical Systems Group within the Advanced Computing in Health (ACH) section of the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL) seeks a motivated Research Professional. This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling of complex biosystems. The successful candidate will also contribute to efforts that bridge molecular, cellular, and systems-level modeling, with growing relevance to emerging paradigms such as whole-cell modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing resources and collaborating across ORNL, federal agencies, and academic partners.

Requirements

Ph.D. (within 0–5 years) in computational bioscience, computational biophysics, computer science, or a related field
Strong programming skills in C++, Python, or similar scientific computing languages
Extensive experience with MD simulation tools such as NAMD, GROMACS, AMBER, or LAMMPS, and visualization tools (e.g., VMD, PyMOL)
Experience working on high-performance computing (HPC) systems
Demonstrated ability to conduct independent research with a strong publication record
Demonstrated ability to obtain research funding (DoE, DoD, NIH, NSF etc)
Excellent written and verbal communication skills for interdisciplinary collaboration
Commitment to ORNL’s core values: Impact, Integrity, Teamwork, Safety, and Service

Nice To Haves

Deep expertise in atomistic and multiscale simulation methods (e.g., MD, enhanced sampling, QM/MM)
Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering, GPU/accelerator optimization, Algorithmic innovation
Experience applying machine learning or AI to molecular simulation, including: Surrogate models or learned potentials, Generative models for biomolecular design, Representation learning for biomolecular systems
Familiarity with protein–protein interaction (PPI) networks, signaling pathways, or systems biology models (experience of bioinformatics tools)
Experience with integrated multiscale modeling frameworks connecting molecular dynamics to cellular or tissue-scale processes
Experience with deep learning frameworks such as PyTorch or TensorFlow
Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving foundation models or scientific LLMs

Responsibilities

Develop and apply scalable molecular dynamics (MD) and multiscale simulation workflows for biomolecular systems (proteins, enzymes, membranes, and complexes)
Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity
Contribute to cross-scale modeling frameworks linking molecular interactions to cellular and network-level behavior (e.g. protein-protein interaction, PPI, network analysis)
Optimize simulation codes and workflows for leadership-class HPC architectures
Collaborate across interdisciplinary teams spanning biology, chemistry, computer science, and applied mathematics
Publish findings in high-impact journals and present at leading conferences