Sort by
Refine Your Search
-
Category
-
Country
-
Program
-
Employer
- ;
- Argonne
- CNRS
- California Institute of Technology
- Inria, the French national research institute for the digital sciences
- Nature Careers
- ; The University of Manchester
- Biology Centre CAS
- Brookhaven Lab
- CEA
- East Carolina University
- Forschungszentrum Jülich
- KU Leuven
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Technical University of Denmark
- University of Glasgow
- AALTO UNIVERSITY
- CERN - European Organization for Nuclear Research
- California State University San Marcos
- Central China Normal University
- DAAD
- Duke University
- ESSEC Business School
- ETH Zurich
- Ecole Centrale de Nantes
- Ecole Nationale Supérieure des Mines de Saint Etienne
- Ecole de l'air et de l'espace
- Harvard University
- Humboldt-Stiftung Foundation
- IMT Atlantique
- IMT Atlantique (Nantes)
- INSA de LYON
- Institut Charles Sadron (CNRS/Université de Strasbourg)
- Instituto Superior Técnico
- Los Alamos National Laboratory
- ONERA
- Oak Ridge National Laboratory
- SciLifeLab
- Simons Foundation/Flatiron Institute
- Texas A&M University
- Télécom Paris
- UNIVERSITY OF STRATHCLYDE
- Universidad Carlos III de Madrid
- University of California
- University of Colorado
- University of Gdansk
- University of Houston Central Campus
- University of Kansas
- University of Lethbridge
- University of Luxembourg
- University of Massachusetts
- University of Washington
- Université Grenoble Alpes
- Université Paris-Saclay GS Sciences de l'ingénierie et des systèmes
- Université de Lille
- Virginia Tech
- 46 more »
- « less
-
Field
-
, numerical optimization, numerical partial differential equations, and parallel computing. The Researcher will join a project developing parallel high-order meshing algorithms from medical images and parallel
-
lack of CFD-grade experimental data for reliable validation of numerical methods. In parallel to the CFD research, the Thermo-Fluids group at the University of Manchester is developing a novel modular
-
and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
-
, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
-
computing Advanced knowledge of numerical methods Geophysical fieldwork experience, preferably with GPR, EMI and ERT Strong English writing skills Since the work involves interdisciplinary cooperation with
-
in a dynamic and collaborative team. In collaboration with the Edinburgh Parallel Computing Centre (EPCC) and our industry partners, the focus of the role is the development of a new solver for
-
and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
-
), numerical methods, and basic knowledge of material science. It is meriting to have one or more of the following skills/qualities: experience and/or thorough understanding of theoretical/numerical methods
-
degree in computer science and: You have a good knowledge of C++ You have skills in software engineering. You are familiar with common development environments and associated tools Knowledge of parallel
-
-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational fluid dynamics, material