Sort by
Refine Your Search
-
Employer
- Carnegie Mellon University
- Oak Ridge National Laboratory
- Stony Brook University
- Northeastern University
- Texas A&M University
- University of Miami
- New York University
- Rutgers University
- SUNY Polytechnic Institute
- University of Houston
- University of Maryland, Baltimore
- University of North Texas at Dallas
- 2 more »
- « less
-
Field
-
Linear and nonlinear photonic device testing Finite element analysis (FEA) Micro‑ and nano‑fabrication Knowledge in quantum optics/physics is a strong plus Qualifications: PhD required A combination of
-
mechanics, FEA, CFD, or equivalent field. Proficiency in one or multiple finite element analysis software (e.g., ABAQUS, LS-Dyna, ANSYS), and in computational fluid dynamic software (e.g., Fluent, CFX
-
testing and computational simulations (e.g., finite element analysis, fluid-structure interaction). This work will contribute to improving our understanding of valve biomechanics, inform device design, and
-
(photolithography, metal evaporation, etching) Experience with packaging schemes such as flip-chip bonding, anisotropic conductive film bonding and wire bonding Finite element Method (FEM) simulations (MEMS, Electro
-
to: i) lead the design and creation of an experimental setup to examine fluid-structure interactions between 3D-printed models of deep sea sponges and ii) lead the formulation and analysis of finite
-
., Multiphysics finite element analysis, Matlab, Labview etc.) cleanroom experience, and characterization of electronic devices are required. Further, knowledge of system level integration and haptics feedback in
-
, anisotropic conductive film bonding and wire bonding Finite element Method (FEM) simulations (MEMS, Electro-static and Quasi-static simulations) Discrete electronic design (Analog and digital design using COTS
-
. Proficiency in CAD/CAM and finite-element modeling is required, alongside disciplined verification/validation practices and the ability to translate prototypes into reliable, user-ready systems. Demonstrated
-
structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
-
characterization, mechanical testing, 3D microstructural analysis, finite element simulations, atomistic modeling, and thermal transport measurement techniques to advance mechanistic understanding and predictive