Group items tagged

The Statistics Program supports research in statistical theory and methods, including research in statistical methods for applications to any domain of science and engineering. The theory forms the base for statistical science. The methods are used for stochastic modeling, and the collection, analysis and interpretation of data. The methods characterize uncertainty in the data and facilitate advancement in science and engineering. The Program encourages proposals ranging from single-investigator projects to interdisciplinary team projects.

The Statistics Program supports research in statistical theory and methods, including research in statistical methods for applications to any domain of science and engineering. The theory forms the base for statistical science. The methods are used for stochastic modeling, and the collection, analysis and interpretation of data. The methods characterize uncertainty in the data and facilitate advancement in science and engineering. The Program encourages proposals ranging from single-investigator projects to interdisciplinary team projects.

The purpose of this Funding Opportunity Announcement (FOA) is to invite exploratory/developmental research grant (R21) applications for the development of innovative methods and algorithms in biomedical computing, informatics, and data science addressing priority needs across the cancer research continuum, including cancer biology, cancer treatment and diagnosis, cancer prevention, cancer control and epidemiology, and/or cancer health disparities. As a component of the NCI's Informatics Technology for Cancer Research (ITCR) Initiative, this FOA encourages applications focused on the development of novel computational, mathematical, and statistical algorithms and methods that can considerably improve acquisition, management, analysis, and dissemination of relevant data and/or knowledge. The central mission of ITCR is to promote research-driven informatics technology across the development lifecycle to address priority needs in cancer research. In order to be successful, the proposed informatics method or algorithm must have a clear rationale on why it is novel and how it will benefit the cancer research field.

The Statistics Program supports research in statistical theory and methods, including research in statistical methods for applications to any domain of science and engineering. The theory forms the base for statistical science. The methods are used for stochastic modeling, and the collection, analysis and interpretation of data. The methods characterize uncertainty in the data and facilitate advancement in science and engineering. The Program encourages proposals ranging from single-investigator projects to interdisciplinary team projects.

Supports mathematical research in areas of science where computation plays a central and essential role, emphasizing design, analysis, and implementation of numerical methods and algorithms, and symbolic methods. The prominence of computation with analysis of the computational approach in the research is a hallmark of the program. Proposals ranging from single-investigator projects that develop and analyze innovative computational methods to interdisciplinary team projects that not only create and analyze new mathematical and computational techniques but also use/implement them to model, study, and solve important application problems are encouraged.

Supports mathematical research in areas of science where computation plays a central and essential role, emphasizing design, analysis, and implementation of numerical methods and algorithms, and symbolic methods.  The prominence of computation with analysis of the computational approach in the research is a hallmark of the program.  Proposals ranging from single-investigator projects that develop and analyze innovative computational methods to interdisciplinary team projects that not only create and analyze new mathematical and computational techniques but also use/implement them to model, study, and solve important application problems are encouraged.

A. Proposals for the development of novel collaborative perception algorithms that will enable heterogeneous teams of UxS (specifically unmanned air and ground systems) to share knowledge and perform joint target search and tracking autonomously. While existing distributed data fusion methods have looked at probabilistic representations for fusing detections at a decision level, work is needed to investigate shared perceptual features that exist across unmanned air and ground systems to enable the performance of collaborative tasks.  B. Development of a new Rigid Body Dynamics Software Library for Mathematical and Physics-Based Modeling and Simulation. The basic research proposed here would involve the development of a new software library for the temporal (time) integration of the governing equations of rigid body dynamics. The temporal integration technique employed in this new library involves the application of the Runge-Kutta method of various orders and possibly other finite-difference-type techniques to a system of equations consisting of kinematic equations (arising from the Lie Group structure of the group of rotation transformations) which define the first and second time derivatives of the rotation transformation in terms of angular velocity and angular acceleration together with the equations of motion (balance of momentum and balance of angular momentum) of a rigid body. C. Robustness of the Use of Botanical DNA Materials as Anti-Counterfeit Markers for Electronic Components - The project will solicit academic laboratory participation for doing testing that demonstrates the 'robustness' of the use of Botanical DNA material as anti-counterfeit markers for electronic components. Request analysis of candidate DNA marker materials utilizing current DNA manipulation technology. This would be done especially in light of results from (1) above.

This Biological and Environmental Research/Advanced Scientific Computing Research (BERASCR) Scientific Discovery Thru Advanced Computing (SciDAC) Partnership FOA will enable scientists to conduct complex scientific and engineering computations at a level of fidelity needed to simulate real-world climate conditions, by supporting deep, necessary, and productive collaborations between climate scientists on the one hand and applied mathematicians and computer scientists on the other, that overcome the barriers between these disciplines and consequently fully exploit the capabilities of Department of Energy (DOE) High Performance Computing (HPC) systems in order to accelerate advances in climate science. This SciDAC opportunity targets three particular topics of high-priority for DOE climate research that are expected to be transformed by effective climate-computational partnerships: the development of new and innovative methods to predict sea-level change; the development of a theoretical statistical-numerical framework to improve climate prediction; and the development of improved methods for model component coupling. The next-generation climate model capabilities will contribute to the newly launched Accelerated Climate Model for Energy (ACME) and further its progress toward design of climate codes for leadership class computers and in support of energy science and mission requirements.

Mathematical Physics develops and applies advanced mathematical methods to enable the solution of difficult problems in physics.  It often is the work of mathematicians with a strong physics interest and intuition, or of physicists who are also highly regarded in mathematics.  Very advanced mathematical methods are applied (by individuals or collaborators) to important but difficult physics concepts to rigorously establish the behavior of theoretical systems,  resolve conundrums or find new directions.  The PHY Mathematical Physics program is dedicated to supporting such research.

The goals of the Program are to: (i) advance knowledge about the processes that force and regulate the atmosphere’s synoptic and planetary circulation, weather and climate, and (ii) sustain the pool of human resources required for excellence in synoptic and global atmospheric dynamics and climate research.Research topics include theoretical, observational and modeling studies of the general circulation of the stratosphere and troposphere; synoptic scale weather phenomena; processes that govern climate; the causes of climate variability and change; methods to predict climate variations; extended weather and climate predictability; development and testing of parameterization of physical processes; numerical methods for use in large-scale weather and climate models; the assembly and analysis of instrumental and/or modeled weather and climate data; data assimilation studies; development and use of climate models to diagnose and simulate climate and its variations and change.Some Climate and Large Scale Dynamics (CLD) proposals address multidisciplinary problems and are often co-reviewed with other NSF programs, some of which, unlike CLD, use panels in addition to mail reviewers, and thus have target dates or deadlines. Proposed research that spans in substantive ways topics appropriate to programs in other divisions at NSF, e.g., ocean sciences, ecological sciences, hydrological sciences, geography and regional sciences, applied math and statistics, etc., must be submitted at times consistent with target dates or deadlines established by those programs. If it's not clear whether your proposed research is appropriate for co-review, please contact CLD staff (listed above) or the potential co-reviewing program staff (including but not limited to)Eric Itsweire (Physical Oceanography), eitsweir@nsf.govL. Douglas James (Hydrological Sciences), ldjames@nsf.govThomas Baerwald (Geography and Regional Sciences), tbaerwal@nsf.govTom Russell (Applied and Computational Math),

The goals of the Program are to: (i) advance knowledge about the processes that force and regulate the atmosphere’s synoptic and planetary circulation, weather and climate, and (ii) sustain the pool of human resources required for excellence in synoptic and global atmospheric dynamics and climate research. Research topics include theoretical, observational and modeling studies of the general circulation of the stratosphere and troposphere; synoptic scale weather phenomena; processes that govern climate; the causes of climate variability and change; methods to predict climate variations; extended weather and climate predictability; development and testing of parameterization of physical processes; numerical methods for use in large-scale weather and climate models; the assembly and analysis of instrumental and/or modeled weather and climate data; data assimilation studies; development and use of climate models to diagnose and simulate climate and its variations and change. Some Climate and Large Scale Dynamics (CLD) proposals address multidisciplinary problems and are often co-reviewed with other NSF programs, some of which, unlike CLD, use panels in addition to mail reviewers, and thus have target dates or deadlines. Proposed research that spans in substantive ways topics appropriate to programs in other divisions at NSF, e.g., ocean sciences, ecological sciences, hydrological sciences, geography and regional sciences, applied math and statistics, etc., must be submitted at times consistent with target dates or deadlines established by those programs. If it's not clear whether your proposed research is appropriate for co-review, please contact CLD staff.

The Defense Sciences Office at the Defense Advanced Research Projects Agency (DARPA) is soliciting innovative research proposals to develop novel mathematical methods, on both the theoretical and algorithmic fronts, which will solve high-dimensional dynamic data-driven optimization and decision-making problems. Proposed research should fully address challenges that arise from the nonlinear, nonconvex, hybrid (continuous, discrete) nature of underlying modeling and optimization of realistic complex application problems. Specifically excluded is research that offers existing solutions and optimization methods for application areas.

This Funding Opportunity Announcement (FOA) encourages applications to develop valid markers to assess the activity of fundamental aging mechanisms in humans that may influence the risk and progression of multiple aging conditions. Projects are encouraged that focus on selected mechanism(s) that may regulate aging changes, assess multiple possible markers for these mechanisms, test methods to improve their measurement properties, characterize their variability among individuals of differing ages and within the same age cohort, and assess their relationships in humans to in vivo functions influenced by the mechanism(s) under study. It is strongly encouraged that each project includes an interdisciplinary research team with expertise, as needed, in the biology of their selected mechanism(s), biomedical aging research, clinical pathology including laboratory assays, imaging methods, human cohort studies, tissue banking, biorepository resources, and statistics. Though the principal focus of the initiative is on development of markers in humans, studies in laboratory animals may also be conducted when necessary for the development of human markers, and potential development of parallel laboratory animal markers of a given mechanism.

Growing Convergence Research (GCR) at the National Science Foundation was identified as one of 10 Big Ideas. Convergence research is a means for solving vexing research problems, in particular, complex problems focusing on societal needs. It entails integrating knowledge, methods, and expertise from different disciplines and forming novel frameworks to catalyze scientific discovery and innovation. GCR identifies Convergence Research as having two primary characteristics: Research driven by a specific and compelling problem. Convergence Research is generally inspired by the need to address a specific challenge or opportunity, whether it arises from deep scientific questions or pressing societal needs. Deep integration across disciplines. As experts from different disciplines pursue common research challenges, their knowledge, theories, methods, data, research communities and languages become increasingly intermingled or integrated. New frameworks, paradigms or even disciplines can form sustained interactions across multiple communities. A distinct characteristic of convergence research, in contrast to other forms of multidisciplinary research, is that from the inception, the convergence paradigm intentionally brings together intellectually diverse researchers and stakeholders to frame the research questions, develop effective ways of communicating across disciplines and sectors, adopt common frameworks for their solution, and, when appropriate, develop a new scientific vocabulary. Research teams practicing convergence aim at developing sustainable relationships that may not only create solutions to the problem that engendered the collaboration, but also develop novel ways of framing related research questions and open new research vistas.

Nanomanufacturing is the production of useful nano-scale materials, structures, devices and systems in an economically viable manner. The NSF Nanomanufacturing Program supports fundamental research in novel methods and techniques for batch and continuous processes, top-down (addition/subtraction) and bottom-up (directed self-assembly) processes leading to the formation of complex heterogeneous nanosystems. The program supports basic research in nanostructure and process design principles, integration across length-scales, and system-level integration. The Program leverages advances in the understanding of nano-scale phenomena and processes (physical, chemical, electrical, thermal, mechanical and biological), nanomaterials discovery, novel nanostructure architectures, and new nanodevice and nanosystem concepts. It seeks to address quality, efficiency, scalability, reliability, safety and affordability issues that are relevant to manufacturing. To address these issues, the Program encourages research on processes and production systems based on computation, modeling and simulation, use of process metrology, sensing, monitoring, and control, and assessment of product (nanomaterial, nanostructure, nanodevice or nanosystem) quality and performance.

Nanomanufacturing is the production of useful nano-scale materials, structures, devices and systems in an economically viable manner. The NSF Nanomanufacturing Program supports fundamental research in novel methods and techniques for batch and continuous processes, top-down (addition/subtraction) and bottom-up (directed self-assembly) processes leading to the formation of complex heterogeneous nanosystems. The program supports basic research in nanostructure and process design principles, integration across length-scales, and system-level integration. The Program leverages advances in the understanding of nano-scale phenomena and processes (physical, chemical, electrical, thermal, mechanical and biological), nanomaterials discovery, novel nanostructure architectures, and new nanodevice and nanosystem concepts. It seeks to address quality, efficiency, scalability, reliability, safety and affordability issues that are relevant to manufacturing. To address these issues, the Program encourages research on processes and production systems based on computation, modeling and simulation, use of process metrology, sensing, monitoring, and control, and assessment of product (nanomaterial, nanostructure, nanodevice or nanosystem) quality and performance.

The purpose of this Funding Opportunity Announcement (FOA) is to support the development of advanced computational, bioinformatic and statistical tools to determine the functional relevance of genetic variants associated with mental disorders of complex etiologies identified through genome-wide association or sequencing studies. The overarching goal of this initiative is to support the development of innovative computational methods that facilitate the elucidation of the functionality of genetic variants associated with mental illness, taking into account the added complexities and nuances of brain diseases, and to ultimately inform novel treatment development based on human biology.

This FOA solicits new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. Proposed tools could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. It is expected that the tools developed under this FOA will be made widely available to the neuroscience research community for their use and modification. Investigative studies should be limited to validity testing of the tools being developed.

This FOA solicits new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. Proposed tools could include the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. It is expected that the tools developed under this FOA will be made widely available to the neuroscience research community for their use and modification. Investigative studies should be limited to validity testing of the tools being developed.

he Advanced Scientific Computing Research (ASCR) program of the Office of Science (SC), U.S. Department of Energy (DOE), hereby invites applications for basic research in novel mathematical and statistical methods, models, and tools for the representation, analysis, and understanding of DOE data-centric science at scale. In particular, this FOA is seeking long-term research that will impact DOE data-centric science challenges related to orders of magnitude improvement in modeling and simulation capabilities on next-generation computer architectures and next-generation science facilities producing dramatic increases in data velocity, volume, and complexity.

This Funding Opportunity Announcement (FOA) aims to stimulate the integration of data across HIV research networks and cohorts as well as the development, adaptation and application of state-of-the-art analytic methods to achieve a better understanding of the various factors that characterize neurobehavioral and psychosocial functioning of people living with HIV or those at risk for HIV.