Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- Nature Careers
- ;
- ; The University of Manchester
- DAAD
- Technical University of Denmark
- University of Sheffield
- ; Swansea University
- ; University of Surrey
- Utrecht University
- ; Brunel University London
- ; Cranfield University
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Oxford
- ; University of Sheffield
- Ariel University
- Chalmers University of Technology
- Ecole Polytechnique Federale de Lausanne
- Empa
- Ghent University
- Leibniz
- MASARYK UNIVERSITY
- Max Planck Institute for Sustainable Materials •
- Monash 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 Copenhagen
- University of Louisville
- University of Nottingham
- University of Twente
- 27 more »
- « less
-
Field
-
“lattice” version of space and time, similar to the finite difference approach in computational fluid dynamics. Using this Lattice QCD method, Centre Vortex fields will be analysed to understand particles
-
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
-
Work group: Institute of Coastal Ocean Dynamics Area of research: Other Part-Time Suitability: The position is suitable for part-time employment. Starting date: 12.06.2025 Job description: PhD
-
. The solution relies on the integration of a biosensor into an aerosol sampler. This interdisciplinary project brings together excellent research teams from fluid dynamics, bioengineering and biotechnology. Your
-
metamorphic conditions, the exact mechanisms (dissolution–precipitation vs. dynamic recrystallization vs. mechanical transport vs. partial melting), the extent of mobility and role of fluids remain debated
-
partners in the European project, in particular also with the research partner at the Royal Military Academy in Belgium, who is doing the Computational Fluid Dynamics (CFD) simulations to estimate
-
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
-
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
-
experience in computational modelling. It will involve the use of open-source computational fluid dynamics codes, with turbulence modelling and porous media approaches. It will also require the development
-
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