Quantum Information Science (QIS) supports theoretical and experimental proposals that explore quantum applications to new computing paradigms or that foster interactions between physicists, mathematicians, and computer scientists that push the frontiers of quantum-based information, transmission, and manipulation. The quantum information science program is focused on investigations relevant to disciplines supported by the Physics Division, while encouraging broader impacts on other disciplines.
The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical Computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.
The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical Computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.
The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical Computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.
The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical Computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.
The Office of Science (SC) of the Department of Energy hereby announces its continuing interest in receiving grant applications for support of work in the following program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, and Nuclear Physics.
The Office of Science (SC) of the Department of Energy hereby announces its continuing interest in receiving grant applications for support of work in the following program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, and Nuclear Physics.
The Mechanics of Materials program supports fundamental research on the behavior of solid materials and respective devices under external actions.?? A diverse and interdisciplinary spectrum of research is supported with emphasis placed on fundamental understanding that i) advances theory, experimental, and/or computational methods in Mechanics of Materials, and/or ii) uses contemporary Mechanics of Materials methods to address modern challenges in material and device mechanics and physics. Proposed research can focus on existing or emerging material systems across time and length scales. Intellectual merit typically includes advances in fundamental understanding of deformation, fracture, fatigue, and contact through constitutive modeling, multiscale and multiphysics analysis, computational methods, or experimental techniques.??Recent interests comprise, but are not limited to:?? contemporary materials including multiphase materials and material systems, soft materials, active materials, low-dimensional materials, phononic/elastic metamaterials, friction, wear;??multiphysics methods, mechanics at the nano, meso and microscale and multiscale integration thereof, as well as approaches incorporating fundamental understanding of physics and chemistry into the continuum-level understanding of the response characteristics of materials and material systems.
The National Science Foundation's (NSF's) Directorates for Computer & Information Science & Engineering (CISE) and Mathematical & Physical Sciences (MPS) recently launched the Transdisciplinary Research in Principles of Data Science (TRIPODS) Phase I program with the goal of promoting long-term, interdisciplinary research and training activities that engage theoretical Computer scientists, statisticians, and mathematicians in developing the theoretical foundations of data science. Twelve TRIPODS Phase I Institutes were established in FY17 (see https://www.nsf.gov/news/news_summ.jsp?cntn_id=242888).
The Partnerships between Science and Engineering Fields and the NSF TRIPODS Institutes (TRIPODS + X) solicitation seeks to expand the scope of the TRIPODS program beyond the foundations community by engaging researchers across other NSF disciplines and the TRIPODS research teams in collaborative activities. TRIPODS + X projects will foster relationships between researchers in science & engineering domains and foundational data scientists by leveraging existing NSF investments in the TRIPODS organizations. Working in concert with a TRIPODS organization, a TRIPODS + X project would focus on data-driven research challenges motivated by applications in one or more science and engineering domains or other activities aimed at building robust data science communities.
This funding opportunity announcement (FOA) invites applications for mentored career enhancement (K18) awards in research areas that are highly relevant to the NIH BRAIN Initiative. This career enhancement program will support development of research capability for the BRAIN Initiative, with specific emphasis on cross-training independent investigators in a substantively different area of neuroscience, neuroethics, or in a quantitative and physical discipline (e.g., physics, chemistry, engineering, computer science, mathematics); and vice versa, cross-training independent investigators trained in a quantitative or physical discipline proposing to gain in-depth training in a high-priority area of neuroscience. The research project conducted under this K18 should enhance the candidate's ability to significantly contribute to or lead projects that investigate questions central to the goals of the BRAIN Initiative. Eligible candidates are independent investigators at any faculty rank or level.
This funding opportunity announcement (FOA) invites applications for mentored career enhancement (K18) awards in research areas that are highly relevant to the NIH BRAIN Initiative. This career enhancement program will support development of research capability for the BRAIN Initiative, with specific emphasis on cross-training independent investigators in a substantively different area of neuroscience, neuroethics, or in a quantitative and physical discipline (e.g., physics, chemistry, engineering, computer science, mathematics); and vice versa, cross-training independent investigators trained in a quantitative or physical discipline proposing to gain in-depth training in a high-priority area of neuroscience. The research project conducted under this K18 should enhance the candidate's ability to significantly contribute to or lead projects that investigate questions central to the goals of the BRAIN Initiative. Eligible candidates are independent investigators at any faculty rank or level.
The High-Performance Computing for Manufacturing (HPC4Mfg) Program seeks qualified industry partners to participate in short-term, collaborative projects with the Department of Energy's (DOE's) national laboratories. Through support from the Advanced Manufacturing Office of the DOE Office of Energy Efficiency and Renewable Energy (EERE), selected industry partners will be granted access to High Performance Computing (HPC) facilities and experienced staff at DOE National Laboratories. The collaborations will address key challenges in U.S. manufacturing by apply modeling, simulation, and data analysis to the manufacturing of materials with the intent to improve energy efficiency, increase productivity, reduce cycle time, enable next-generation technologies, test control system algorithms, investigate intensified processes, lower energy cost, and accelerate innovation. Projects must demonstrate potential impact to energy efficiency in manufacturing and/or the development of new clean energy technologies with a potential for broad national impact. We solicit proposals in the following primary areas:
The overarching goals of this program are to (i) prepare, nurture, and grow the national scientific research workforce for creating, utilizing, and supporting advanced cyberinfrastructure (CI) that enables potentially transformative fundamental science and engineering research and contributes to the Nation's overall economic competiveness and security; (ii) ensure broad adoption of CI tools, methods, and resources by the fundamental science and engineering research community to enable new modes of discovery; and (iii) integrate core literacy and discipline-appropriate advanced skills in advanced CI as well as computational and data science and engineering into the Nation's educational curriculum/instructional material fabric spanning undergraduate and graduate courses. For the purpose of this solicitation, advanced CI is broadly defined as the set of resources, tools, and services for advanced computation, data handling, networking, and security that collectively enable potentially transformative fundamental research.
The program is intended to support high-risk theoretical mathematics, physics and computer science projects of exceptional promise and scientific importance on a case-by-case basis.
Funding and Allowable Expenses
The Targeted Grant in MPS program provides funding for up to five years. The funding level and duration is flexible and should be appropriate based on the type of support requested in the proposal. There is no recommended or assumed funding level for this program.
Allowable expenses include:
* Up to one month of summer salary and related benefits per year for the PI and any co-Investigator(s). These salary funds are not substitutional (cannot be used to relieve a university of salary costs) and cannot be used to reduce teaching loads below the departmental norm. They can only be used to supplement the salary similarly to a summer salary in the U.S. system.
* Domestic or international travel for the PI and co-Investigator(s).
* Research equipment, experiments, computations and other expenses directly benefiting the research.
* Salary support and related benefits, including tuition support, for staff/research scientists, postdoctoral fellows and research associates, graduate students or undergraduate research assistants.
CMMT supports theoretical and computational materials research in the topical areas represented in DMR's Topical Materials Research Programs (these are also variously known as Individual Investigator Award (IIA) Programs, or Core Programs, or Disciplinary Programs), which include: Condensed Matter Physics (CMP), Biomaterials (BMAT), Ceramics (CER), Electronic and Photonic Materials (EPM), Metals and Metallic Nanostructures (MMN), Polymers (POL), and Solid State and Materials Chemistry (SSMC). The CMMT program supports fundamental research that advances conceptual understanding of hard and soft materials, and materials-related phenomena; the development of associated analytical, computational, and data-centric techniques; and predictive materials-specific theory, simulation, and modeling for materials research. First-principles electronic structure, quantum many-body and field theories, statistical mechanics, classical and quantum Monte Carlo, and molecular dynamics, are among the methods used in the broad spectrum of research supported in CMMT. Research may encompass the advance of new paradigms in materials research, including emerging data-centric approaches utilizing data-analytics or machine learning.
The CISE-MPS Interdisciplinary Faculty Program in Quantum Information Science is designed to promote research in the area of Quantum Information Science (QIS) by providing resources to allow QIS researchers and researchers from the CISE or MPS disciplines to actively engage in joint research efforts, addressing problems at the interface between the mathematical and physical sciences and computer and information sciences through long-term visits by faculty to a host institution.
The purpose of the Mentored Quantitative Research Career Development Award (K25) is to attract to NIH-relevant research those investigators whose quantitative science and engineering research has thus far not been focused primarily on questions of health and disease. The K25 award will provide support and protected time for a period of supervised study and research for productive professionals with quantitative (e.g., mathematics, statistics, economics, computer science, imaging science, informatics, physics, chemistry) and engineering backgrounds to integrate their expertise with NIH-relevant research. Prospective candidates are encouraged to contact the relevant NIH staff for IC-specific programmatic and budgetary information: Table of IC-Specific Information, Requirements and Staff Contacts.
Our Data-Driven Discovery Initiative seeks to advance the people and practices of data-intensive science, to take advantage of the increasing volume, velocity, and variety of scientific data to make new discoveries. Within this initiative, we're supporting data-driven discovery investigators - individuals who exemplify multidisciplinary, data-driven science, coalescing natural sciences with methods from statistics and computer science.
These innovators are striking out in new directions and are willing to take risks with the potential of huge payoffs in some aspect of data-intensive science. Successful applicants must make a strong case for developments in the natural sciences (biology, physics, astronomy, etc.) or science enabling methodologies (statistics, machine learning, scalable algorithms, etc.), and applicants that credibly combine the two are especially encouraged. Note that the Science Program does not fund disease targeted research.
It is anticipated that the DDD initiative will make about 15 awards at ~$1,500,000 each, at $200K-$300K/year for five years.
The Office of Science of the Department of Energy hereby invites grant applications for support under the Early Career Research Program in the following program areas: Advanced Scientific Computing Research (ASCR); Biological and Environmental Research (BER); Basic Energy Sciences (BES), Fusion Energy Sciences (FES); High Energy Physics (HEP), and Nuclear Physics (NP). The purpose of this program is to support the development of individual research programs of outstanding scientists early in their careers and to stimulate research careers in the areas supported by the DOE Office of Science.
The Office of Fusion Energy Sciences (FES) of the Office of Science (SC), U.S. Department of Energy (DOE), announces its interest in receiving new or renewal grant applications for theoretical and computational research relevant to the U.S. magnetic fusion energy sciences program. Applications selected in response to this FOA will be funded in Fiscal Year 2015, subject to the availability of appropriated funds. The specific areas of interest are: 1. Macroscopic Stability 2. Confinement and Transport 3. Boundary Physics 4. Plasma Heating & Non-inductive Current Drive, and 5. Energetic Particles Specific information about each topical area is included in the Supplementary information section of the full Funding Opportunity Announcement (FOA) document. Due to the limited availability of funds, Principal Investigators with continuing theory grants may not submit a new application in the same topical areas as their existing grant. A Principal Investigator may submit only one application in response to this FOA, but applications can target multiple topical areas.
The Geospace Section of the Division of Atmospheric and Geospace Sciences, to ensure the health and vitality of solar and space sciences on university teaching faculties, is pleased to offer awards for the creation of new tenure-track faculty positions within the intellectual disciplines which comprise the space sciences. The aim of these awards is to integrate research topics in solar and space physics into basic physics, astronomy, electrical engineering, geoscience, meteorology, computer science, and applied mathematics programs, and to develop space physics graduate programs capable of training the next generation of leaders in this field. Space Science is interdisciplinary in nature and the Faculty Development in the Space Sciences awardees will be expected to establish partnerships within the university community.
The purpose of the Mentored Quantitative Research Career Development Award (K25) is to attract to NIH-relevant research those investigators whose quantitative science and engineering research has thus far not been focused primarily on questions of health and disease. The K25 award will provide support and protected time for a period of supervised study and research for productive professionals with quantitative (e.g., mathematics, statistics, economics, computer science, imaging science, informatics, physics, chemistry) and engineering backgrounds to integrate their expertise with NIH-relevant research.
