Sort by
Refine Your Search
-
Postdoctoral Research Associate - Structural Simulation and Machine Learning (ML) for Polymer Compos
process optimization framework for polymer composites through physics-based simulations, sensors from machines, and artificial intelligence. Our research division carries out high quality R&D focused
-
environmental samples, conduct controlled mesocosm experiments, develop and maintain environmental sensors in field and mesocosm settings, and analyze geochemical properties of environmental samples in
-
research. The BTSD couples basic and applied research to discover, design, and develop new materials, sensors, electronic and mechanical devices/systems, and processes to enable new and improved energy
-
liquids, 2D materials and combinations of these materials with electrodes and integrated sensors for follow-on characterization and testing. Utilize existing cleanroom facilities and expertise within
-
-day operations, you will have access to state-of-the-art manufacturing systems and material characterization equipment, as well as access to an extensive suite of sensors, embedded hardware, and high
-
spin-based quantum sensors. Maintain and perform research with cryogenic optical microscopes. Present and report research results and publish scientific results in peer-reviewed journals in a timely
-
multidisciplinary teams that are composed of ORNL researchers with various backgrounds (e.g., material science, automation, advanced software, and sensors and controls among others). Interact and collaborate with
-
, implement, and apply novel machine-learning (ML) and statistical methods to sensor and component health monitoring with reporting, anomaly detection, and fault isolation of complex dynamic systems. Develop
-
sensors. The proposed solutions will include GPU-based HPC solutions that can be applicable for several geospatially focused research and development projects in energy, transportation, national security
-
sensors. The proposed solutions will include GPU-based HPC solutions that can be applicable for several geospatially focused research and development projects in energy, transportation, national security