DOE Mathematical Multifaceted Integrated Capability Centers (MMICCs): Harvard Pre-Proposal Deadline

Date: 

Monday, May 2, 2022 (All day)

DOE Mathematical Multifaceted Integrated Capability Centers (MMICCs)
Harvard Pre-Proposal Deadline: May 2, 2022
FAS/SEAS/OSP Pre-Application Deadline: May 12, 2022
Sponsor Pre-Application Deadline: May 17, 2022
FAS/SEAS/OSP Full Proposal Deadline: June 21, 2022
Sponsor Full Proposal Deadline: June 28, 2022
Award Amount: Up to $1,200,000/year for five years

The DOE SC program in Advanced Scientific Computing Research (ASCR) invites applications for basic research that address fundamental challenges within DOE’s mission areas of energy (as detailed by the Energy Earthshots Initiative), environment, and security, and from a perspective that requires new integrated efforts across multiple mathematical, statistical, and computational disciplines. This FOA invites applications for new Mathematical Multifaceted Integrated Capability Centers (MMICCs) to enable greatly enhanced scientific discovery, design, optimization or decision-support capabilities for the increasingly complex systems, processes, and problems that arise in science and energy research. Proposed research tightly focused on the solution of a particular science or engineering problem are outside the scope of this solicitation.

 

These MMICCs will enable applied mathematics researchers to work together in large, collaborative teams to develop the mathematics needed to address significant scientific computing research challenges. The MMICCs allow researchers to take a broader view of the problem as a whole, and devise solution strategies that attack the problem in its entirety by building fundamental, multidisciplinary mathematical capabilities and tools cognizant of both existing and emerging computing paradigms. The MMICCs teams will have the flexibility and technical expertise to consider all aspects of the problem-solving process simultaneously ⎼ ranging from the mathematical formulation to the development, analysis, integration of appropriate models and methods, and demonstration of results and capabilities.