Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Portugal
- United Kingdom
- Germany
- Sweden
- France
- Netherlands
- Spain
- Belgium
- Poland
- Denmark
- Norway
- Singapore
- Italy
- United Arab Emirates
- Australia
- Morocco
- Finland
- Switzerland
- Romania
- Czech
- Luxembourg
- Austria
- Lithuania
- Macau
- Canada
- Estonia
- Brazil
- China
- Japan
- Croatia
- Greece
- Cyprus
- Hong Kong
- Ireland
- Taiwan
- Andorra
- Israel
- Latvia
- Mexico
- Vietnam
- Worldwide
- Europe
- Hungary
- India
- Malta
- Sao Tome and Principe
- Saudi Arabia
- Slovenia
- 39 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Economics
- Biology
- Science
- Materials Science
- Mathematics
- Chemistry
- Business
- Education
- Environment
- Electrical Engineering
- Earth Sciences
- Arts and Literature
- Psychology
- Law
- Physics
- Humanities
- Social Sciences
- Linguistics
- Philosophy
- Sports and Recreation
- Statistics
- 14 more »
- « less
-
specimens. The postdoc will contribute to the development of hybrid modeling and identification approaches that combine classical constitutive frameworks, numerical simulation, and machine learning. The work
-
methods for multiphase flow models Integrate experimental, pilot-scale, or high-fidelity simulation data into model calibration and validation workflows Design and run numerical simulations of multiphase
-
, pilot-scale, or high-fidelity simulation data into model calibration and validation workflows Design and run numerical simulations of multiphase flow systems and reactors Quantify model uncertainty and
-
models to realistic urban environments. The effects of short-term microclimatic variability and vehicle-induced vortices on VTOL UAV operations shall be quantified. high-fidelity CFD simulations and
-
analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
-
response to climate forcings for different past climates. · Perform and analyze global model simulations. · Collaborate with IPSL Earth System model developers to ensure consistent integration
-
collaborators. The activities will include: • Electromagnetic modeling and numerical simulations (e.g., FDTD, FEM) • Design of metasurface architectures based on dielectric materials available at CRHEA
-
machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with
-
of Pisa) and Dr. Stan Van Gisbergen (SCM, Holland) https://www.scm.com/ , who will also serve as industrial mentor. DC9 - Objectives: Apply Machine Learning force fields and sampling methods to model bio
-
Keen to push the frontiers of multiphase reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering! About us Our