Post-Doctoral Research Associate: Department of Physics and Astronomy - UTK

Job Description

The Biophysics Group at the University of Tennessee is seeking a highly motivated and dependable Postdoctoral Researcher with a background in physics, engineering, chemistry, or a related quantitative field who is interested in self-organization phenomena in living systems. The research will focus primarily on the dynamic assembly and regulation of bacterial cell-division machinery.

Applicants with expertise in theoretical biophysics, computational modeling, molecular simulation, scientific machine learning, or quantitative bacterial cell biology will receive strong consideration. Candidates should demonstrate a strong publication record appropriate to their career stage, substantial experience in scientific programming, and the ability to independently formulate, execute, document, and complete computational research projects. The position requires strong scientific judgment, consistent research productivity, clear communication, and effective collaboration in an interdisciplinary environment.

The successful candidate will join a growing interdisciplinary biophysics research group in the Department of Physics & Astronomy. The postdoctoral researcher will take primary responsibility for computational and theoretical projects investigating the self-organization and regulation of bacterial cell-division networks, with particular emphasis on the dynamics of bacterial division machinery. The researcher will be expected to work with a high degree of independence, establish reproducible computational workflows, maintain organized research records and code, communicate progress regularly, and carry projects from model development and simulation through analysis, interpretation, and publication.

The position provides opportunities to integrate statistical-physics theory, molecular and coarse-grained simulation, and machine-learning approaches while collaborating with experimental and computational researchers at the University of Tennessee and Oak Ridge National Laboratory.

• Develop and apply theoretical and computational models of bacterial cell division and self-organization.
• Independently design, execute, troubleshoot, and analyze large-scale computational simulations.
• Develop reproducible and well-documented simulation, analysis, and scientific-software workflows.
• Apply AI/ML methods where scientifically appropriate to model development, parameter inference, or simulation analysis.
• Critically evaluate simulation results, identify technical or conceptual problems, and propose appropriate next steps.
• Maintain organized research records, code repositories, simulation data, and documentation sufficient for reproducibility and project continuity.
• Communicate research progress, challenges, and results clearly and regularly within the research group.
• Collaborate effectively with experimental groups to develop testable connections between modeling predictions and biophysical measurements.
• Take substantial responsibility for preparing manuscripts and moving projects toward timely peer-reviewed publication.
• Present research at conferences and scientific meetings.
• Mentor undergraduate researchers and contribute constructively to the intellectual environment of the group.

• Education:
  
  Ph.D. in Physics, Biophysics, Computational Chemistry, Engineering, or a closely related field (degree must be conferred by the start date).
• Experience:
  
  Demonstrated research experience in theoretical/computational modeling of biological or soft-matter systems.
• Knowledge, Skills, Abilities:
  • Strong coding proficiency (e.g., Python, C++, or Fortran).
  • Experience with molecular simulation packages (LAMMPS, GROMACS, or similar).
  • Strong written and oral communication skills.
  • Ability to work both independently and collaboratively in a multidisciplinary team.

• Education:
  
  Ph.D. with emphasis in Biophysics, Computational Biology, or Soft-Matter Physics.
• Experience:
  • Research background in cytoskeletal networks, bacterial cell biology, or self-assembly.
  • Experience with AI/ML methods for force-field development or data-driven modeling.
  • Prior mentoring of junior researchers.
• Knowledge, Skills, Abilities:
  • Familiarity with high-performance computing environments.
  • Ability to bridge theory and experiment in interdisciplinary collaborations.

• Knoxville, Tennessee (University of Tennessee, Department of Physics & Astronomy).
• Onsite position; limited hybrid flexibility may be considered.