Computational Biologist (ML) Postdoctoral Researcher

LLNL•Livermore, CA

3d•Hybrid

About The Position

Lawrence Livermore National Laboratory (LLNL) is seeking a highly motivated Postdoctoral Researcher to conduct research in computational structural biology. The role involves developing methods for predicting protein-protein interactions in host-pathogen systems using deep learning (DL). While current tools excel at monomeric structure prediction and known complexes, they struggle to identify interacting versus non-interacting proteins. This position will focus on developing specific models to discriminate between interacting and non-interacting proteins. The researcher will be part of an interdisciplinary team, leveraging computational tools and developing new DL-based approaches for predicting binary interactions, specificity, and structure. Collaboration with existing computational biologists will be key to developing next-generation protein design models and tools. The role requires regular presentation of work, publication of research findings, and occasional travel. This position is within the Computational Engineering Division (CED), Engineering Directorate.

Requirements

PhD in Life Sciences, Computational Biology, or Life-science applied ML, Statistics, Computer Science or Mathematics, or a related technical or scientific field.
Experience developing and implementing deep learning models and algorithms, extracting embeddings, or fine-tuning models using modern software libraries such as PyTorch, or similar as evidenced through publications or software releases.
Experience working with protein sequence and structure; knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.
Ability to work independently on defined research projects, as well as a member of a team with a diverse set of scientists, engineers, and other technical and administrative staff.
Programming experience with Python and expertise with UNIX and high-performance computing environments.
Ability to develop independent research projects as demonstrated through publication of peer-reviewed manuscripts.
Ability to travel as necessary.

Nice To Haves

Understanding and experience in protein bioinformatics, protein structure prediction, and/or protein function prediction.
Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows.
Experience in collaborating with experimental and computational biologists.

Responsibilities

Conduct research, and contribute to designing, analyzing, and extending DL-based tools for prediction and optimization of protein interaction specificity.
Participate in the development of protein sequence and structure computational frameworks and analysis tools.
Collaborate with external partners (Universities, Industry, other National Laboratories) to advance computational biology simulation efforts.
Prepare complex and detailed progress reports, written analyses, and verbal briefings to support project needs and deadlines and to present research results to sponsors.
Independently pursue the development of new and innovative research methods relevant to the needs of Laboratory programs and/or external funding agencies.
Contribute to proposals and statements of work.
Publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, and/or technical meetings.
Travel as needed to coordinate with research collaborators and to attend external meetings and conferences.
Perform other duties as assigned.