Opportunity at National Institute of Standards and Technology NIST
Application of Knowledge Graphs to Materials Science and Engineering
Material Measurement Laboratory, Materials Measurement Science Division
Please note: This Agency only participates in the February and August reviews.
|Zachary Tim Trautt
The application of artificial intelligence (AI) in experimental material measurement science involves the union of physical infrastructure (e.g., synthesis and characterization equipment, robotics), cyberinfrastructure (e.g., databases, high-performance computing, collaboration tools), and humans (e.g., scientists, engineers, students, managers). The recent interest in Explainable AI (XAI) motivates research in Knowledge Representation and Reasoning (KRR). The successful applicant would have an opportunity to apply their expertise in semantic web, linked data, and knowledge graphs to the field of materials science and engineering. The successful applicant would work with a team of experts including experimental materials scientists, computational materials scientists, machine learning experts, computer scientists, data scientists, and data engineers to define and implement community standards for knowledge representation and interchange. The successful applicant will conduct novel research in autonomous reasoning and XAI, and would help design and develop software and systems to enable the adoption of KRR technologies within materials science and engineering. The successful applicant does not need specific knowledge in materials science and engineering, but should have a strong background in KRR, semantic web, linked data, and/or knowledge graphs.
Knowledge Representation and Reasoning; KRR; Artificial Intelligence; AI; Machine Learning; ML; Semantic Web; Linked Data; Knowledge Graph; Materials Science and Engineering; High-throughput Experimental Science
Open to U.S. citizens
Open to Postdoctoral applicants