Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- France
- Germany
- United Kingdom
- Portugal
- Sweden
- Norway
- United Arab Emirates
- Belgium
- Spain
- Netherlands
- Poland
- Singapore
- Italy
- Luxembourg
- Austria
- Denmark
- China
- Finland
- Switzerland
- Czech
- Ireland
- Australia
- Japan
- Romania
- Croatia
- Canada
- Cyprus
- Greece
- Israel
- Morocco
- Saudi Arabia
- Estonia
- Hong Kong
- Iceland
- Slovakia
- South Africa
- Taiwan
- Andorra
- Armenia
- Brazil
- Bulgaria
- India
- Malta
- Mexico
- New Zealand
- Sao Tome and Principe
- Slovenia
- 38 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Economics
- Mathematics
- Materials Science
- Science
- Biology
- Business
- Earth Sciences
- Environment
- Chemistry
- Education
- Physics
- Law
- Psychology
- Social Sciences
- Arts and Literature
- Electrical Engineering
- Sports and Recreation
- Linguistics
- Humanities
- Design
- Philosophy
- Statistics
- 15 more »
- « less
-
%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
-
is to develop high-fidelity models based on a test-calculation dialogue, seeking the best compromise between the degree of accuracy, the level of complexity, and the effort required to identify
-
observations disponibles dans les observatoires de l'infrastructure de recherche OZCAR comme l'Observatoire du Larzac (https://deims.org/83b01fa5-747f-47be-9185-408d73a90fb2
-
(LES) results. Key Responsibilities: Develop and refine numerical algorithms for real-time wind field forecasting. Validate forecasting models against high-fidelity LES data and field measurements
-
to overcome the limitations of single-panel partitions. The work will involve analytical and numerical modeling of wave propagation and transmission, the design of hybrid ABH architectures, and the optimization
-
effects, never observed before in an experimental setting, through numerical modelling and laboratory experiments. This PhD thesis is part of a collaboration with the École Normale Supérieure de Lyon and
-
technology transfers. Potentially collaborate with fabrication and experimental teams to validate models on real hardware and datasets. Where to apply Website https://careers.hpe.com/us/en/job/1204598/HPE-Labs
-
kinetic models and their hydrodynamic limits. Dispersive phenomena in kinetic theory. Quantum gases. Applicants must hold a PhD degree in mathematics, physics or a related field. We seek candidates with
-
carbon cycling, with a focus on boreal forest ecosystems Experience with data processing software for eddy-covariance data (EddyPro, REddyProc) Excellent numeric and analytical problem-solving skills
-
tomography, computational resources, and laboratory facilities for experimental mechanics? The Division of Solid Mechanics conducts research within constitutive modelling, nonlinear numerical methods