LLNL
Research
AI/MLComputationalBiologist-PostdoctoralResearcher
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“AI/ML Computational Biologist - Postdoctoral Researcher at LLNL. Skills: Machine learning, Computational biology, Multiomic data, Deep learning. Develop and apply machine learning methods. Contribute to workflow design and implementation”
What You'll Achieve.
Identify molecular programs associated with disease progression; Generate biologically meaningful insights; Develop predictive models that generalize across biological systems; Publish results in peer-reviewed journals
Industry & Context.
Develop predictive models; Generate biologically meaningful insights
Two-year term appointment, Possibility of extension to three years, Must be eligible to access Laboratory in compliance with NDAA Section 3112
What They're Looking For.
Must Have
PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, Data Science, or related field, Background in machine learning, statistical modeling, computational biology, or related quantitative discipline, Experience analyzing high-dimensional biological data, Proficiency in Python and R, Experience with ML frameworks such as PyTorch, TensorFlow, or similar, Familiarity with Linux/Unix and scientific computing workflows, Demonstrated ability to conduct high-quality research and publish results, Demonstrated ability to work effectively in a collaborative research environment, Written and verbal communication skills
Nice to Have
Experience with deep learning or probabilistic modeling approaches, Experience with single-cell, spatial, and/or multimodal omics data, Experience with multiomic data integration, Experience with transfer learning, domain adaptation, cross-dataset integration, or batch correction, Experience with transformers, self-supervised learning, or pretrained models for biological data, Experience training and scaling machine learning models on large datasets, Interest in immunology, host-pathogen biology, or disease modeling
What You'll Do.
Develop and apply machine learning methods
Contribute to workflow design and implementation
and apply approaches for multimodal data
adapt and evaluate self-supervised and foundation models
Develop and apply interpretable models
Process and analyze large-scale sequencing and omics datasets
Present research findings at seminars
Contribute to research design and project execution
Collaborate in a multidisciplinary team environment
Publish results in peer-reviewed journals
Perform other duties as assigned
How You'll Work.
Team & Collaboration
Multidisciplinary team environment
Communication Scope
Written communication; Verbal communication; Present research findings
Full Job Description
Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We are seeking a Postdoctoral Research Staff Member to conduct research at the intersection of artificial intelligence (AI), machine learning (ML), and computational biology. The successful candidate will apply and adapt state-of-the-art AI/ML approaches to understand host-response dynamics in complex biological systems. Research will focus on integrating large-scale multiomic datasets, including bulk, single-cell, spatial, and multimodal data, from animal models and human cohorts to identify molecular programs associated with disease progression, immune responses, and biological resilience. The candidate will leverage modern machine learning approaches, including deep learning, self-supervised learning, and biological foundation models, to generate biologically meaningful insights from diverse datasets and develop predictive models that generalize across biological systems. This position is in the Integrative Multi-Omics Group and offers the opportunity to work under the guidance of senior scientists on high-dimensional biological data at scale in a collaborative, multidisciplinary environment. This is a two-year term appointment with the possibility of extension to a maximum of three years. In this role you will * Develop and apply machine learning methods for prediction and representation learning from high-dimensional biological data. * Contribute to the design and implementation of workflows for integrative
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