Sort by
Refine Your Search
-
. This position resides in the Multifunctional Equipment Integration Group in the Thermal Systems Science Section, Buildings and Transportation Science Division, Energy Science and Technology Directorate at Oak
-
Requisition Id 16798 Overview: The Data and AI Systems Research Section/Workflow systems Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is
-
Qualifications: A Ph.D. degree in electrical engineering, or related discipline completed within the last five years. Expertise in power systems and power electronics. Experience in C/C++, Matlab, and Python
-
correlated electron systems Proficiency with scripting or programmatic languages, such as Python, c, and matlab Excellent written and oral communication skills. Motivated self-starter with the ability to work
-
-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified
-
robotics, controls, autonomous systems, sensor fusion, mapping, or a related area. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and to
-
microbial systems relevant to the emerging bioeconomy. These tools will accelerate our ability to interrogate genotype–phenotype relationships, expand fundamental understanding of microbial physiology, and
-
the Integrated Building Deployment and Analysis group in the Buildings Materials & System Integration Section, Building and Transportation Science Division, Energy Science and Technology Directorate at Oak Ridge
-
Requisition Id 16841 Overview: Oak Ridge National Laboratory is the largest US Department of Energy science and energy Laboratory, conducting basic and applied research to deliver transformative
-
and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in