Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- France
- Portugal
- Germany
- Sweden
- Netherlands
- Norway
- Spain
- Belgium
- Denmark
- Italy
- Singapore
- Australia
- Finland
- Ireland
- Switzerland
- Luxembourg
- China
- Czech
- Canada
- Estonia
- Morocco
- Poland
- Austria
- Japan
- Hong Kong
- United Arab Emirates
- Brazil
- Malta
- Vietnam
- Andorra
- Macau
- Saudi Arabia
- Barbados
- Bulgaria
- Iceland
- Latvia
- Lithuania
- Romania
- Slovenia
- 31 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Biology
- Medical Sciences
- Economics
- Science
- Materials Science
- Mathematics
- Earth Sciences
- Chemistry
- Environment
- Business
- Arts and Literature
- Humanities
- Linguistics
- Law
- Physics
- Psychology
- Electrical Engineering
- Social Sciences
- Sports and Recreation
- Education
- Design
- Philosophy
- 14 more »
- « less
-
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
-
datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and
-
W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
clinical application. The focus is particularly on photon-counting computed tomography (PCCT), 4D MRI flow imaging, and AI-supported analysis and modeling methods (e.g., CT-FFR, predictive software models
-
knowledge of statistical and computational research methods (e.g., predictive/user modeling), cognition/affect tracking, measurement theory, analyzing high-frequency time series data, experience with
-
: Textual Prediction of Survival (LLM classification & Attention Modelling) This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods
-
predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results. Where to apply
-
process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
-
of concrete samples by alternating short-term model predictions and accelerated aging experiments on reconstructed aged-equivalent samples. The methods to develop and adopt will be: for O1, literature review
-
Reactor) designs. This system constitutes a corium containment strategy in the event of a severe accident. Therefore, understanding the thermodynamic properties of the corium is essential for predicting its
-
. The core of the methodology involves building an analytics framework for the modeling and prediction of application metrics in the CEC, where node attributes such as workload characteristics and network