Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
- Computer Science
- Engineering
- Biology
- Medical Sciences
- Science
- Economics
- Materials Science
- Earth Sciences
- Mathematics
- Business
- Chemistry
- Electrical Engineering
- Linguistics
- Environment
- Arts and Literature
- Education
- Law
- Physics
- Philosophy
- Psychology
- Social Sciences
- Sports and Recreation
- 12 more »
- « less
-
interaction scores. Build and deploy machine learning and statistical models for functional genomics predictions, including sgRNA efficiency and drug sensitivity scoring. Collaborate with laboratory members
-
(SHM), physics-based modeling, and data-driven analytics to enable predictive, performance-based decision-making and improve infrastructure safety, resilience, and lifecycle performance. The candidate is
-
to identify those most at risk from extreme heat, as well as offering personalized adaptation advice --- translating rich multi-modal data into interpretable, scalable prediction and advising models. ICARUS
-
of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
-
Distributed, robust and adaptive model predictive control (MPC) School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr P Trodden Application Deadline: Applications
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
-
and measurement noise. • Developing predictive control strategies based on reinforcement learning or hybrid approaches compatible with real-time adaptive optics constraints. • Proposing and
-
on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
-
statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
-
NIST only participates in the February and August reviews. The fire modeling community is actively working to develop the tools needed to quantitatively predict material and product flammability