Postdoc in Membrane Biophysics & Multiscale Simulations

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 85,050 - 103,950; Full time

Benefits offered by this job

Medical insurance (PPO/HD)

Dental and vision coverage

Competitive 401(k) with match

Tuition assistance

Job summary

Los Alamos National Laboratory seeks a postdoctoral researcher to advance computational biophysics at the interface of membranes, statistics, and biophysics. You will build multiscale workflows to simulate complex membrane systems, including lipid platforms, viral/bacterial membranes, and organelles.

The role requires atomistic and coarse-grained membrane-protein MD experience, Python analytics, and familiarity with GROMACS, CHARMM36m, MARTINI, and ML-accelerated methods.

Qualifications

• PhD in a related field completed within the last 5 years or near completion.
• Experience in atomistic protein-membrane simulations (CHARMM36m preferred).
• Experience in coarse-grained protein-membrane simulations (MARTINI preferred).
• Ability to code complex analysis in Python.
• Familiarity with GROMACS.
• Experience with ML-accelerated simulations.

Responsibilities

• Develop multiscale simulation workflows for complex membrane biomolecular systems.
• Model lipid-based platforms, viral and bacterial membranes, and organelle membranes.
• Investigate carbohydrate-containing systems, glycoproteins and glycolipids in MD simulations.

Skills

Atomistic simulations

Coarse-grained simulations

Python coding

Enhanced sampling

GROMACS

ML acceleration

Education

PhD in related field

Tools

CHARMM36m

MARTINI

Job description

Los Alamos National Laboratory seeks a postdoctoral researcher to advance computational biophysics at the interface of membranes, statistics, and biophysics. You will build multiscale workflows to simulate complex membrane systems, including lipid platforms, viral/bacterial membranes, and organelles.

The role requires atomistic and coarse-grained membrane-protein MD experience, Python analytics, and familiarity with GROMACS, CHARMM36m, MARTINI, and ML-accelerated methods.