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Lawrence Livermore National Laboratory · 15 hours ago

Bioinformatic Scientist

Livermore, CA, USA

Full-time

Hybrid

Senior Level

$115K/yr - $214K/yr

Lawrence Livermore National Laboratory (LLNL) is dedicated to strengthening the United States’ security through innovative research. They are seeking a Bioinformatic Scientist to conduct research and evaluate bioinformatic tools while collaborating with experts in various scientific fields to analyze large datasets and develop new methodologies for pathogen detection.

Information TechnologyMarket ResearchSecurity

Growth Opportunities

No H1B

Security Clearance Required

U.S. Citizen Only

Responsibilities

Analyze metagenomic datasets from environmental (e.g., wastewater, aerosol) and clinical (nasal swab, saliva, etc) samples to determine taxonomy, function and predict impact

Research, develop, and implement computational and statistical techniques for analyzing novel and unknown sequences

Conduct multiomic data analysis including genomics, transcriptomics, proteomics and metabolomics for biomarker discovery

Contribute to biological data management such as metadata schemas, databases, data catalogs and integrated workflows

Contribute to and influence the development of innovative projects, principles, and ideas in biosurveillance, biodetection and microbiome

Routinely interact with technical contacts at sponsor and partner organizations; represent the organization on specific technical projects

Determine, propose, and implement advanced analysis methodologies and contribute to identifying future research directions and proposals that will secure future projects in the field

Contribute to the completion of project milestones, balance multiple projects/tasks and priorities to ensure deadlines are met, working independently with minimal direction within the scope of assignments

Lead the development of manuscripts and presentations documenting project activities and research results

Oversee the activities of other personnel, providing informal mentoring and guidance to less-experienced team members

Perform other duties as assigned

Qualification

BioinformaticsMachine LearningData ManagementMetagenomicsHigh-Performance ComputingStatistical TechniquesMultiomic Data AnalysisCommunication SkillsInterpersonal SkillsMentoring

Required

Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship

PhD in Bioinformatics, Computational Biology, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or a related field

Comprehensive knowledge and experience developing and applying methods in one or more of the following bioinformatic areas: metagenomics, transcriptomics, metabolomics, proteomics, taxonomy and function analysis, multimodal data integration

Experience in large data management such as metadata schema development, data cataloging, database development, and integration with pipelines, AI/ML models and workflows

Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or running numerical simulations of complex workflows

Advanced verbal and written communication skills necessary to collaborate within a team environment and present technical information to varied audiences

Effective interpersonal skills and initiative necessary to interact with all levels of personnel and work independently in a collaborative, multidisciplinary team environment

Demonstrated ability to balance multiple projects and prioritize competing demands while maintaining high-quality standards for deliverables

Demonstrated ability to provide guidance and informal mentoring to other personnel and junior team members

Demonstrated ability to represent the organization as a primary technical contact and to contribute to the development of innovative projects, principles, and ideas

Preferred

Domain knowledge in protein and genome language models sufficient to communicate effectively with team members and subject matter experts

Experience and advanced knowledge in developing and applying algorithms in advanced machine learning areas

Experience developing and implementing machine learning models such as deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning