52 applied-physic Postdoctoral positions at Oak Ridge National Laboratory in Postdoctoral
-
the Nanomaterials Chemistry Group in the Separations and Polymer Chemistry Section, Chemical Sciences Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL). As part of our research team
-
Requisition Id 16939 Overview: Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative
-
Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems
-
Materials Science Section, Materials Science and Technology Division, Physical Sciences Directorate at Oak Ridge National Laboratory (ORNL). As part of our research team, you will closely collaborate with a
-
. This position resides in the Manufacturing Systems Analytics group in the Digital and Secure Manufacturing Section, Manufacturing Science Division, Energy Science and Technology Directorate (ESTD) at Oak Ridge
-
-, p- and f-block radioactive ions of interest in nuclear medicine. This position resides in the Chemical Separations Group in the Chemical Sciences Division, Physical Sciences Directorate (PSD) at Oak
-
navigation, trajectory planning, and point cloud mapping; and (3) deploy the developed algorithms on a physical mobile robot operating in GPS-denied, low-light, and geometrically repetitive environments
-
the Materials Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). The selected candidate will work with multiple other groups within MSTD
-
, radiological health, medical physics, nuclear engineering, applied mathematics or a closely related discipline) Sound foundation in radiation transport, behavior of radionuclides in biological systems, and/or
-
Requisition Id 16715 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifing frameworks for high-temperature alloys