Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Chalmers University of Technology
- CNRS
- Nature Careers
- University of North Carolina at Chapel Hill
- University of Southern Denmark
- Aarhus University
- Argonne
- Babes-Bolyai University
- Brandeis University
- EPFL
- Georgetown University
- KTH Royal Institute of Technology
- Luleå University of Technology
- Oak Ridge National Laboratory
- Princeton University
- Technical University of Denmark
- Technical University of Munich
- University of Lund
- Université de Bordeaux / University of Bordeaux
- Utrecht University
- 10 more »
- « less
-
Field
-
develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
-
) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
-
being part of these two networks. Subject description The project involves mathematical modelling of complex fluid dynamics, in particular viscoelastic flows in nano-structures. The work involves
-
APPFL and OmniFed. Major Duties and Responsibilities: Conduct and publish original research focused on data readiness methodologies and frameworks for scalable AI applications across fluid dynamics
-
substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
-
of the following qualifications: a PhD in fluid dynamics, oceanography, physics, applied mathematics or similar field; affinity with coastal simulations; strong skills in Python programming; motivation to cooperate
-
focuses on building scalable, accreditation-ready analysis workflows to detect and classify microplastics in complex sample types such as drinking water, plant-based beverages, and biological fluids. As a
-
substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
, dynamical systems, and scientific computation. Ideal experimentalists will have strong skills in experimental fluid mechanics, including laboratory design, high-speed imaging, flow visualization, and