PhD Intern- Autonomous Intelligence

<strong>Overview</strong><br><br><p>At Pacific Northwest National Laboratory (PNNL), our core capabilities are organized into major departments called Directorates, each focused on a specific area of scientific research or function, with its own leadership team and dedicated budget.<br />Our Science & Technology Directorates include:</p><ul><li>National Security</li><li>Earth and Biological Sciences</li><li>Physical and Computational Sciences</li><li>Energy and Environment</li></ul><p>Additionally, we host the Environmental Molecular Sciences Laboratory, a DOE Office of Science user facility located on the PNNL campus.<br /><br />The <strong>Physical and Computational Sciences Directorate (PCSD) </strong>combines strengths in experimental, computational, and theoretical chemistry and materials science with advanced computing, applied mathematics, and data science capabilities. These resources are central to PNNL’s discovery mission. Our greatest asset is our people—experts across diverse scientific disciplines who collaborate to tackle the most significant scientific challenges of our time.<br /><br />Within PCSD, the <strong>Advanced Computing, Mathematics, and Data Division (ACMDD)</strong> conducts basic and applied research in artificial intelligence, applied mathematics, computing technologies, and data and computational engineering. Our teams apply end-to-end co-design principles to advance energy-efficient computing systems and develop next-generation algorithms to analyze, model, and control complex systems in science, energy, and national security.<br /><br /></p><p><strong>Responsibilities</strong><br />As a PhD Intern – Autonomous Intelligence, you will:</p><ul><li>Conduct theoretical and research-driven investigations into advanced AI and autonomous systems.</li><li>Develop and analyze novel algorithms, architectures, and mathematical models for autonomous intelligence.</li><li>Explore foundational concepts in graph representation learning, graph neural networks, and knowledge graph construction.</li><li>Advance research in scientific machine learning, semantic reasoning, and large-scale AI models.</li><li>Perform conceptual modeling, simulation studies, and algorithmic proofs to validate theoretical approaches.</li><li>Contribute to peer-reviewed publications, technical reports, and presentations for scientific audiences.</li><li>Collaborate with multidisciplinary teams to integrate theoretical insights into broader research initiatives.</li></ul><p><span data-ccp-props=