Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- NTNU - Norwegian University of Science and Technology
- Cranfield University
- Chalmers University of Technology
- Inria, the French national research institute for the digital sciences
- NTNU Norwegian University of Science and Technology
- Utrecht University
- ; Imperial College London
- ; Swansea University
- ; University of Birmingham
- ; University of Leeds
- ; University of Southampton
- ; University of Warwick
- Aix-Marseille Université
- Curtin University
- Delft University of Technology (TU Delft)
- ETH Zurich
- ETH Zürich
- Eindhoven University of Technology (TU/e)
- Forschungszentrum Jülich
- Ghent University
- IMT Atlantique
- INSA de LYON
- RMIT University
- Technical University of Denmark
- University of Adelaide
- University of Strasbourg
- University of Twente
- 17 more »
- « less
-
Field
-
This PhD project will focus on developing AI-based methods to accelerate the Swansea University in-house discontinuous Galerkin (DG) finite element solver for the Boltzmann-BGK (BBGK) equation
-
in an engineering or related subject with experience of mechanics, finite element methods and numerical analysis. Please state your entry requirements plus any necessary or desired background A first
-
finite element methods, which demand extensive data and are costly, PINNs embed governing physical laws directly into the learning process. This allows effective management of limited and noisy data
-
. Develop analytical and finite element (FE) models to investigate the extent and sources of nonlinear behaviour in LGSs. 3. Develop novel control strategies to stabilise LGS shape, orbit & attitude
-
techniques — as well as theoretical and computational techniques that may include finite element methods, crystal plasticity theory, damage theory, molecular dynamics and advanced multiscale modelling methods
-
-speed cameras (in a newly renovated lab dedicated to our research group). A significant component of the analysis will include image processing, including data-driven methods and machine learning. You
-
documented experience in at least one and preferably more of the following areas: computational solid- and/or biomechanics; finite strain hype elasticity; finite element methods; numerical optimization methods
-
utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images
-
experience in microstructural analyses. Familiarity with mechanical testing procedures and, ideally, experience in numerical simulation (e.g., finite element methods). Strong analytical skills, an independent
-
outcomes of this research will be well-validated computational analysis methods to predict failure mode, and quantify defect and damage produced in fibre metal laminate composites. The models can be used