Postdoc: Deep Learning for Protein Interactions

Lawrence Livermore National Laboratory; Livermore (CA); Hybrid; USD 122,000 - 143,000; Full time

Benefits offered by this job

Relocation Assistance

Education Reimbursement Program

Flexible schedules

401(k)

Job summary

Lawrence Livermore National Laboratory in California seeks a Postdoctoral Researcher to advance computational structural biology by developing deep learning methods for predicting protein-protein interactions in host-pathogen systems.

You will build models to discriminate interacting versus non-interacting proteins and collaborate with computational and experimental biologists to push the boundaries of protein design.

Qualifications

• PhD in Life Sciences, Computational Biology, or Life-science applied ML, Statistics, Computer Science, Mathematics, or related field.
• Experience developing and implementing deep learning models and algorithms using PyTorch or similar libraries.
• Experience with protein sequence and structure; bioinformatics and structure modeling knowledge.

Responsibilities

• Design, analyze, and extend DL-based tools for predicting protein interaction specificity.
• Develop protein sequence and structure computational frameworks and analysis tools.
• Collaborate with external partners to advance computational biology efforts.
• Prepare detailed progress reports and present results to sponsors.

Skills

Deep learning

Python

UNIX / HPC

Independent research

Education

PhD in Life Sciences/Computational Biology

Tools

PyTorch

High-performance computing (HPC)

Job description

Lawrence Livermore National Laboratory in California seeks a Postdoctoral Researcher to advance computational structural biology by developing deep learning methods for predicting protein-protein interactions in host-pathogen systems.

You will build models to discriminate interacting versus non-interacting proteins and collaborate with computational and experimental biologists to push the boundaries of protein design.