Idaho National Laboratorys (INL) Computational Mechanics and Materials Department (CMM), within the Nuclear Science and Technology Directorate (NS&T), is seeking an exceptional Computational Scientist/Fuel Performance Modeler to provide assistance regarding fuel performance modeling activities at INL, the expansion of INL's nationally recognized fuel performance analysis presence, and the pursuit of existing NS&T programmatic missions and emerging opportunities.This research work will build on Bison, a finite element-based nuclear fuel performance code applicable to a variety of fuel forms including light water reactor fuel rods, TRISO particle fuel, and metallic rod and plate fuel.

Job Summary ExxonMobil is looking for a full-time staff position in the area of Computational Sciences in Houston, Texas.We are looking for dynamic, creative, and resourceful individuals to join our research team in the general area of mathematical modeling of physical processes.

UES, Inc. is seeking a Scientist in the areas of computational physics, machine learning and optimization, to serve as a collaborative member of the Soft Matter Branch within the Materials and Manufacturing Directorate of the Air Force Research Laboratory at Wright Patterson Air Force Base in Dayton, OH.Advances in additive manufacturing (AM) techniques have significantly broadened the realizable design space for complex geometries and material property distributions, such as gradients in stiffness, permittivity and coefficient of thermal expansion.

UES, Inc. is seeking a Scientist in the areas of computational physics, machine learning and optimization, to serve as a collaborative member of the Soft Matter Branch within the Materials and Manufacturing Directorate of the Air Force Research Laboratory at Wright Patterson Air Force Base in Dayton, OH.Advances in additive manufacturing (AM) techniques have significantly broadened the realizable design space for complex geometries and material property distributions, such as gradients in stiffness, permittivity and coefficient of thermal expansion.

PUBLIC TRUST: NONE REQUISITION TYPE: REGULAR At GDIT people are our differentiator.As an HPC (High Performance Computing) Computational Scientist / Engineer you will be trusted to support our HPC PET program with locations across the USA.

PUBLIC TRUST: NONE REQUISITION TYPE: REGULAR At GDIT people are our differentiator.As an HPC (High Performance Computing) Computational Scientist / Engineer you will be trusted to support our HPC PET program with locations across the USA.

Description Job Description: The Leidos Innovations Center (LInC) has an opening for a Scientist/Computational Geophysicist in our Arlington VA office with magnetic sensor and algorithm development experience to support a range of multi-phase sensor development programs.This is an exciting opportunity to use your scientific knowledge of geophysical principles in the modeling and simulation of geophysical signatures generated from various sources including earthquakes explosions and other natural and artificial sources.

Description Job Description: The Leidos Innovation Center (LInC) is seeking a Computational Physicist to join our growing high-performance sensors research team in the development and application of advanced sensing technologies.Primary Responsibilities Develop physics-based numerical models of various materials and devices Serve as subject matter expert on multi-physics modeling of materials semiconductor devices and laser-material interactions Utilize commercial software such as Comsol or Apsys to investigate novel material physics and to add understanding to experimental results Help identify experiments necessary to validate material models and provide physics-based predictions for outcomes To be successful in this role the following must apply to you: U.S.-born citizen with the ability to obtain a Top Secret clearance BS in physics or engineering with 10 years experience MS with 5 years experience or PhD with 2 years experience Demonstrated knowledge of materials science including elemental and compound semiconductors; quantum wells wires and dots; multi-quantum wells and strained-layer superlattices Experience modeling materials and devices using commercially available software Experience modeling laser-material interactions Familiarity with semiconductor fabrication methods Familiarity with semiconductor device applications such as focal plane arrays light emitting diodes VCSELs etc.