Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- Technical University of Denmark
- ;
- ; The University of Manchester
- DAAD
- Nature Careers
- University of Sheffield
- Utrecht University
- ; Brunel University London
- ; Swansea University
- ; University of Surrey
- RMIT University
- Technical University of Munich
- University of Adelaide
- ; Cranfield University
- ; The University of Edinburgh
- ; University of Oxford
- ; University of Sheffield
- ; University of Warwick
- Ariel University
- Chalmers University of Technology
- ETH Zurich
- 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 Antwerp
- University of Cambridge
- University of Copenhagen
- University of Louisville
- University of Nottingham
- University of Twente
- 30 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
-
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
-
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
-
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
-
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
-
Dynamics , Condensed Matter Theory , Cosmology , Crystallography , Dark Matter , Data analysis , EIC , Electron Hydrodynamics , electron-positron collisions , electron-proton collisions , Electronic Detector
-
of the) [map ] Subject Area: Stochastic dynamical systems Appl Deadline: 2025/06/02 11:59PM (posted 2025/04/16, listed until 2025/06/02) Position Description: Apply Position Description Join Us! Are you
-
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
-
sound background in geophysical fluid dynamics, experience in numerical ocean or atmospheric modelling, and experience with numerical data analysis. Good scientific presentation, writing, and
-
Current modelling and simulations require either generic assumptions to be made for fluid dynamic based modelling leading to inaccuracies between modelled and experimental data or, intense