Scientific Discovery through Advanced Computing: Scientific Machine Learning and Artificial Intelligence for Fusion Energy Sciences
Sector: Government • Location: United States of America
Source: Grants.gov
The DOE SC programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) invite applications under the Scientific Discovery through Advanced Computing (SciDAC) program in the area of Scientific Machine Learning and Artificial Intelligence (ML/AI) for Fusion Energy Science. The goal of this FOA is to support research aiming to sustain and enhance the leadership position
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | archived |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | The DOE SC programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) invite applications under the Scientific Discovery through Advanced Computing (SciDAC) program in the area of Scientific Machine Learning and Artificial Intelligence (ML/AI) for Fusion Energy Science. The goal of this FOA is to support research aiming to sustain and enhance the leadership position of the United States in Artificial Intelligence (AI) while addressing high-priority research opportunities identified in recent fusion community studies. More specific information about the targeted research areas and allowable collaborations between multiple institutions is included in the SUPPLEMENTARY INFORMATION section below. A companion Program Announcement to the DOE National Laboratories (LAB 20-2224) will be posted on the SC Grants and Contracts web site at: https://science.osti.gov/grants/lab-announcements/open |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
Project Type | Obfuscated Data |
Article Published Date | Obfuscated Data |