Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Portugal
- Netherlands
- France
- Germany
- Sweden
- Spain
- Belgium
- Norway
- Denmark
- Singapore
- Italy
- Morocco
- Australia
- Finland
- Switzerland
- Czech
- United Arab Emirates
- Poland
- Canada
- Ireland
- China
- Austria
- Luxembourg
- Romania
- Japan
- Brazil
- Estonia
- Hong Kong
- Andorra
- Croatia
- Greece
- Lithuania
- Malta
- Saudi Arabia
- Slovenia
- 27 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Science
- Materials Science
- Mathematics
- Earth Sciences
- Environment
- Chemistry
- Business
- Psychology
- Humanities
- Education
- Electrical Engineering
- Law
- Physics
- Linguistics
- Social Sciences
- Arts and Literature
- Philosophy
- Statistics
- Sports and Recreation
- 14 more »
- « less
-
close coordination with project partners, the recruited researcher will conduct experiments to determine the extent to which neural models, now at the heart of many approaches to Natural
-
of the system, including laboratory testing and/or in situ monitoring campaigns. •Proposing predictive maintenance strategies based on the collected data and developed models, w ith the aim of optimising
-
, creating predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results
-
Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
-
, numerical methods, and Earth system modeling to develop and evaluate a coupled xylem–phloem transport framework that translates multiscale physics into next-generation vegetation model schemes. Key
-
Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied
-
, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
-
modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
-
position in the area of Learning, Optimization, and Decision Analytics. SCAI (https://scai.engineering.asu.edu/ ), one of the eight Fulton Schools, houses a vibrant Industrial Engineering and Computer
-
believe that generative pre-training offers a promising path to a new class of models that work across settings and can support prediction of many different clinical outcomes at once. To fuel your models