Postdoc, Protein Design & AI-Driven Enzyme Engineering

Los Alamos National Laboratory; Los Alamos (NM); On-site; USD 65,000 - 90,000; Full time

Benefits offered by this job

Relocation assistance

Comprehensive benefits

Onsite gyms and wellness programs

Job summary

Los Alamos National Laboratory in Los Alamos, NM, invites applications for a Postdoctoral Research Associate in the B-IOME group of the Bioscience Division to develop and apply cutting-edge tools for computational protein design and high-throughput screening.

You will generate robust experimental datasets to power machine learning models and accelerate the Design-Build-Test-Learn (DBTL) cycle, collaborating with computational biologists, molecular biologists, and strain engineers to advance

Qualifications

• Protein engineering and biochemistry knowledge including recombinant expression, purification, structural biophysics, and biochemical characterization.
• Understanding of enzyme kinetics, ligand/metal-binding mechanisms, protein stability determinants, and sequence-structure-function relationships.
• Hands-on experience with computational protein design tools and/or high-throughput assay frameworks to generate structured datasets.
• Strong record of scientific productivity and ability to work in an interdisciplinary team.

Responsibilities

• Design and engineer proteins with tailored catalytic efficiency, enhanced stability, or targeted ligand/metal-binding properties.
• Express and characterize engineered proteins in non-model hosts to evaluate functional performance.
• Collaborate with computational biologists, molecular biologists, and strain engineers to advance national security and bioeconomy missions.

Job description

Los Alamos National Laboratory in Los Alamos, NM, invites applications for a Postdoctoral Research Associate in the B-IOME group of the Bioscience Division to develop and apply cutting-edge tools for computational protein design and high-throughput screening.

You will generate robust experimental datasets to power machine learning models and accelerate the Design-Build-Test-Learn (DBTL) cycle, collaborating with computational biologists, molecular biologists, and strain engineers to advance