Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
-
Field
-
influence of pulsed or continuous currents on PPBs during flash sintering. Finite Element modeling using the Abaqus software and a multiphysics framework will be employed to quantify the processes occurring
-
. Knowledge of structural dynamics and experimental testing. Motivation for hands-on laboratory work and independent research. Desirable: Experience with CAD/FEA (Abaqus/ANSYS), MATLAB/Simulink, wind tunnel
-
•Background with relevant packages, (CREO/SOLIDWORKS, ANSYS/ABAQUS, MATLAB) •A driven, professional and self-dependent work attitude is essential •Experience of working within the industry will be an advantage
-
multi-material configurations) using 3D finite element analyses in dedicated tools (e.g., Abaqus) and extract effective properties. Develop and apply hierarchical meso-/macro-scale structural models
-
using Python and Fortran Experience using non-linear finite element software, e.g., Abaqus Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
-
Codes such as ABAQUS, ANSYS, etc. for numerical analysis of fatigue and fracture mechanics Knowledge of basic turbomachinery principles Oral and written proficiency in Norwegian/Scandinavian languages
-
of computational micromechanics. Knowledge of aluminium alloys. Experience using non-linear finite element software, e.g., Abaqus. Experience with programming using Python and Fortran. Experience with conducting
-
. Experience working with Finite Element Method (FEM) tools, such as Abaqus, ANSYS, OrcaFlex, or similar software, is highly regarded. Furthermore, an interest or practical experience in additive manufacturing
-
structural degradation phenomena—including fatigue, corrosion, and biofouling—would also be beneficial. Experience working with Finite Element Method (FEM) tools, such as Abaqus, ANSYS, OrcaFlex, or similar
-
in English Solid knowledge in finite element analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks