Sort by
Refine Your Search
-
Country
-
Employer
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Argonne
- Chalmers University of Technology
- National Aeronautics and Space Administration (NASA)
- ;
- DURHAM UNIVERSITY
- Durham University
- European Space Agency
- Imperial College London
- Nature Careers
- UNIVERSITY OF HELSINKI
- UNIVERSITY OF SYDNEY
- Virginia Tech
- 3 more »
- « less
-
Field
-
candidate will play a key role in developing and advancing new models and simulations for Computational Fluid Dynamics (CFD) hypersonic codes. Specific tasks include developing new turbulence and transition
-
CFD methods DFT and Molecular modelling linked to CCUS Strong analytical and problem-solving skills, with an ability to interpret and analyze simulation and/or experimental results. Ability to work in
-
generates clean energy that can power future HPC systems. You will design, implement and test energy-efficient Computational Fluid Dynamics (CFD) algorithms by leveraging energy-efficient hardware within
-
design, optimization, and sizing using phenomenological and/or CFD methods. Energy system analysis. Strong analytical and problem-solving skills, with an ability to interpret and analyze simulation and/or
-
of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
-
objective will be achieved using engineering and CFD tools capable of addressing high-speed, turbulence, transition, high-speed combustion, and the thermo-mechanical coupling with the thermal protection and
-
ideas/adaptation and development of research protocols. Person Specification Essential Criteria: Qualifications A good first degree in an Engineering related subject. A PhD (or be close to submission) in
-
dynamics (CFD) to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
-
multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
-
, modelling and simulation, CFD, control and experimental investigation would be particularly useful. Candidates close to completion of their PhD will initially be appointed in the junior research training zone