Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- ;
- ; The University of Manchester
- Technical University of Denmark
- DAAD
- Nature Careers
- ; University of Warwick
- RMIT University
- University of Sheffield
- ; Brunel University London
- ; Cranfield University
- ; University of Sheffield
- ; University of Surrey
- University of Adelaide
- University of Twente
- ; Loughborough University
- ; Swansea University
- ; The University of Edinburgh
- Ariel University
- Chalmers University of Technology
- ETH Zurich
- Empa
- Helmholtz-Zentrum Geesthacht
- Leibniz
- MASARYK UNIVERSITY
- Max Planck Institute for Sustainable Materials •
- Monash University
- Mälardalen University
- NTNU - Norwegian University of Science and Technology
- Queensland University of Technology
- Technical University of Munich
- UiT The Arctic University of Norway
- Umeå University
- Universiteit van Amsterdam
- Universiti Teknologi PETRONAS
- University of Antwerp
- University of Cambridge
- University of Copenhagen
- University of Nottingham
- Utrecht University
- 30 more »
- « less
-
Field
-
include: Developing innovative serration and permeable surface designs to further reduce trailing edge noise. Conducting detailed fluid dynamics, aerodynamics, and aeroacoustics investigations to understand
-
Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
-
Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
-
systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing modelling capabilities for the prediction of energy
-
applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
-
research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
-
models for multiphase flows, which are crucial for various industrial processes. The successful candidate will develop advanced physics-based methods in fluid dynamics and heat transfer to study multiphase
-
Dynamics , Condensed Matter Theory , Cosmology , Crystallography , Dark Matter , Data analysis , EIC , Electron Hydrodynamics , electron-positron collisions , electron-proton collisions , Electronic Detector
-
are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
-
prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs