Sort by
Refine Your Search
-
integrated autonomous experimental synthesis and characterization cross-facility agentic-AI platforms that allow real-time guidance and control of these multi-modal experiments for targeted discovery of novel
-
of irradiated ceramics and alloys for tritium technology development using advanced experimental and computational methods. The researcher will perform characterization of model systems using techniques such as
-
Peatland Responses Under Changing Environments) experiment and other DOE-supported observational networks. Major Duties/Responsibilities: Develop, implement, and test new and improved process representations
-
, operations research, or a related quantitative discipline. Demonstrated experience applying Bayesian methods to scientific or engineering problems, including prior specification, likelihood formulation
-
Peatland Responses Under Changing Environments) experiment and other DOE-supported observational networks. Major Duties/Responsibilities: Develop, implement, and test new and improved process representations
-
: Develop physics-based, data-driven, and hybrid (physics-informed ML) models of thermal systems to capture dynamic thermal behavior Validate models against experimental data and refine model accuracy and
-
Qualifications: A PhD in Computational Chemistry, Physics, Chemical Engineering, Materials Science and Engineering, or a related field completed within the last 5 years Experience in theoretical and computational
-
to design and implement experiments, perform data analyses, and interpret experimental results. Excellent interpersonal, oral, and written communication skills. Preferred Qualifications: Demonstrated
-
-institutional collaboration focused on disturbance-driven ecosystem transitions and their impacts across the United States Gulf Coast. EGRET employs an integrated model–experiment (ModEx) approach accelerated by
-
across the U.S. Gulf Coast. EGRET employs an integrated model–experiment (ModEx) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil