Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
-
heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
-
level (neural network models) including plasticity. Electric fields will be estimated based on finite-element method models. The project can be partly adapted to your specific interests and your
-
-aided design (CAD) experience and demonstrated proficiency. Finite-element analysis (FEA) experience and demonstrated proficiency. Required Documents Cover letter summarizing your relevant experience and
-
. Analyze the proposed and optimized solutions. Write reports. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for-recruitment-of-ptgas-staf… Requirements Research FieldEngineering
-
leading group in high-order methods: a class of finite element methods that is now leading the way for future computational fluid dynamics simulations. Specifically, our group develops the Nektar++ spectral
-
structural characterization; magnetic and structural analysis; preliminary experience with finite-element simulations; and experience in developing hardware and software for control and data acquisition as a
-
structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
-
simulations progress, whilst preserving the geometric representation of the project. You will work within a leading group in high-order methods: a class of finite element methods that is now leading the way
-
, plate structures, semantic data models, finite element analysis, and plugin development. The PhD position (full-time) will span 4 years of research and is funded according to standard SNSF regulations