Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- ;
- Technical University of Denmark
- DAAD
- ; The University of Manchester
- Nature Careers
- ; University of Warwick
- University of Sheffield
- ; Cranfield University
- ; University of Sheffield
- ; University of Surrey
- RMIT University
- ; Brunel University London
- ; 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
- UiT The Arctic University of Norway
- Umeå University
- Universiteit van Amsterdam
- Universiti Teknologi PETRONAS
- University of Adelaide
- University of Antwerp
- University of Cambridge
- University of Copenhagen
- University of Nottingham
- Utrecht University
- 28 more »
- « less
-
Field
-
element modeling, computational fluid dynamics). Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization. Experience in supply chains and hygrothermal
-
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
-
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
-
, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
-
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
-
We are seeking an outstanding candidate for a PhD fellowship in the field of computational fluid and solid mechanics. The fellowship will start on September 1st, 2025, or as soon as possible after
-
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
-
flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict multiphase flow behavior. The project also involves applying
-
overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
-
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