TheEngineering of Biomedical Systems program is part of the Engineering Biology and Health cluster, which also includes: 1) the Biophotonics program; 2) the Biosensing program; 3) the Cellular and Biochemical Engineering program; and 4) the Disability and Rehabilitation Engineering program. The goal of theEngineering of Biomedical Systems (EBMS) program is to provide opportunities for creating fundamental and transformative research projects that integrate engineering and life sciences to solve biomedical problems and serve humanity in the long term. Projects are expected to use an engineering framework (for example, design or modeling) that supports increased understanding of physiological or pathophysiological processes. Projects must include objectives that advance both engineering and biomedical sciences. Projects may include: methods, models, and enabling tools applied to understand or control living systems; fundamental improvements in deriving information from cells, tissues, organs, and organ systems; or new approaches to the design of systems that include both living and non-living components for eventual medical use in the long term.
STS is an interdisciplinary field of research that uses historical, philosophical, and social scientific methods to investigate STEM theory and practice. It may focus on history and socio-cultural formation, philosophical underpinnings, or the impacts of science and technology on broader societal concerns including quality of life, ethics, and culture. STS researchers strive to understand the research assumptions of STEM fields, and the co-production of STEM and society, meaning the many ways in which cultural, economic, historical, social and political contexts influence developments in STEM, and how those developments reciprocally influence these contexts.
This program seeks to accelerate fundamental, broad-based research on wireless-specific machine learning (ML) techniques, towards a new wireless system and architecture design, which can dynamically access shared spectrum, efficiently operate with limited radio and network resources, and scale to address the diverse and stringent quality-of-service requirements of future wireless applications. In parallel, this program also targets research on reliable distributed ML by addressing the challenge of computation over wireless edge networks to enable ML for wireless and future applications. Model-based approaches for designing the wireless network stack have proven quite efficient in delivering the networks in wide use today; research enabled by this program is expected to identify realistic problems that can be best solved by ML and to address fundamental questions about expected improvements from using ML over model-based methods.
The purpose of this Funding Opportunity Announcement (FOA) is to stimulate translation of scientific discoveries and engineering developments in imaging, data science and/or spectroscopic technologies into methods or tools that address contemporary problems in understanding the fundamental biology, potential risk of development, diagnosis, treatment, and/or disease status for cancer or other disease.
The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in making research data and artificial intelligence (AI) models findable, accessible, interoperable, and reusable (FAIR1) to facilitate the development of new AI applications in SC's congressionally authorized mission space, which includes the advancement of AI research and development. In particular, ASCR is interested in supporting FAIR benchmark data for AI; and FAIR frameworks for relating data and AI models.
For this FOA, AI is inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). Data, in this context, are the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. An AI model is an inference method that can be used to perform a "task," such as prediction, diagnosis, or classification. The model is developed using training data or other knowledge. An AI task is the inference activity performed by an artificially intelligent system.
The Transformational Tools and Technologies (TTT) Project advances state-of-the-art computational and experimental tools and technologies that are vital to aviation applications in the six strategic thrusts. The project develops new computer-based tools, computational fluid dynamics models, and associated scientific knowledge that will provide first-of-a-kind capabilities to analyze, understand, and predict aviation concept performance. These revolutionary tools will be applied to accelerate NASA's research and the community's design and introduction of advanced concepts. The Project also explores technologies that are broadly critical to advancing ARMD strategic outcomes. Such technologies include the understanding of new types of strong and lightweight materials, innovative controls techniques, and experimental methods. The TTT Materials and Structures Discipline emphasizes improved multifunctional and high temperature materials for airframe and engine application, as well as integrated multiscale modeling and simulation tool development to improve validated first-principles materials and structural modeling.
The National Science Foundation (NSF) through its Divisions of Electrical, Communications and Cyber Systems (ECCS); Chemical, Bioengineering, Environmental and Transport Systems (CBET); Civil, Mechanical and Manufacturing Innovation (CMMI); Information and Intelligent Systems (IIS); and Mathematical Sciences (DMS) announces a solicitation on Multimodal Sensor Systems for Precision Health enabled by Data Harnessing, Artificial Intelligence (AI), and Learning. Next-generation multimodal sensor systems for precision health integrated with AI, machine learning (ML), and mathematical and statistical (MS) methods for learning can be envisioned for harnessing a large volume of diverse data in real time with high accuracy, sensitivity and selectivity, and for building predictive models to enable more precise diagnosis and individualized treatments. It is expected that these multimodal sensor systems will have the potential to identify with high confidence combinations of biomarkers, including kinematic and kinetic indicators associated with specific disease and disability. This focused solicitation seeks high-risk/high-return interdisciplinary research on novel concepts, innovative methodologies, theory, algorithms, and enabling technologies that will address the fundamental scientific issues and technological challenges associated with precision health.
Scientific machine learning is a core component of artificial intelligence and a computational technology that can be trained, with scientific data, to augment or automate human skills. Major research advances will be enabled by harnessing DOE investments in massive amounts of scientific data, software for predictive models and algorithms, high-performance computing (HPC) and networking platforms, and the national workforce. The crosscutting nature of machine learning and AI provides strong motivation for formulating a prioritized research agenda.
Scientific Machine Learning and Artificial Intelligence (Scientific AI/ML) will have broad use and transformative effects across the research communities supported by DOE. Accordingly, a 2019 Basic Research Needs workshop report identified six Priority Research Directions. The first three PRDs describe foundational research themes that are common to the development of Scientific AI/ML methods and correspond to the need for domain-awareness, interpretability, and robustness. The other three PRDs describe capability research themes and correspond to the three major use cases of massive scientific data analysis (PRD #4), machine learning-enhanced
The Foundational Research in Robotics (Robotics) program supports research on robotic systems that exhibit significant levels of both computational capability and physical complexity. For the purposes of this program, a robot is defined as intelligence embodied in an engineered construct, with the ability to process information, sense, and move within or substantially alter its working environment. Here intelligence includes a broad class of methods that enable a robot to solve problems or make contextually appropriate decisions. Research is welcomed that considers inextricably interwoven questions of intelligence, computation, and embodiment. Projects may also focus on a distinct aspect of intelligence, computation, or embodiment, as long as the proposed research is clearly justified in the context of a class of robots.
The focus of the Robotics program is on foundational advances in robotics. Robotics is a deeply interdisciplinary field, and proposals are encouraged that explore the full range of fundamental engineering and computer science research challenges arising in robotics. However, all proposals must convincingly explain how a successful outcome will enable transformative new robot functionality or substantially enhance existing robot functionality. The proposal should clearly articulate how the intellectual contribution of the proposed work addresses fundamental gaps in robotics. Meaningful experimental validation on a physical platform is strongly encouraged. Projects that do not represent a direct fundamental contribution to robotics should not be submitted to the Robotics program.
The Foundational Research in Robotics (Robotics) program supports research on robotic systems that exhibit significant levels of both computational capability and physical complexity. For the purposes of this program, a robot is defined as intelligence embodied in an engineered construct, with the ability to process information, sense, and move within or substantially alter its working environment. Here intelligence includes a broad class of methods that enable a robot to solve problems or make contextually appropriate decisions. Research is welcomed that considers inextricably interwoven questions of intelligence, computation, and embodiment. Projects may also focus on a distinct aspect of intelligence, computation, or embodiment, as long as the proposed research is clearly justified in the context of a class of robots.
The focus of the Robotics program is on foundational advances in robotics. Robotics is a deeply interdisciplinary field, and proposals are encouraged that explore the full range of fundamental engineering and computer science research challenges arising in robotics. However, all proposals must convincingly explain how a successful outcome will enable transformative new robot functionality or substantially enhance existing robot functionality. The proposal should clearly articulate how the intellectual contribution of the proposed work addresses fundamental gaps in robotics. Meaningful experimental validation on a physical platform is strongly encouraged. Projects that do not represent a direct fundamental contribution to robotics should not be submitted to the Robotics program.
Major goals of NSF's D-ISN include:
Improve understanding of the operations of illicit supply networks and strengthen the ability to detect, disrupt, and dismantle them.
Enhance research communities that effectively integrate operational, computational, social, cultural and economic expertise to provide methods and strategies to combat this complex and elusive global security challenge.
Catalyze game-changing technological innovations that can improve discovery and traceability of illicitly sourced products and illicitly sourced labor inputs to products.
Provide research outcomes that inform U.S. national security, law enforcement and economic development needs and policies.
Section 313 of the Omnibus Appropriations Act of 2009 (H.R. 1105, P.L. 111-8) created the Integrated University Program (IUP). DNN R&D is one of the three participants in this program and is continuing a nuclear science and engineering program to support multi-year research projects critical to maintaining the discipline of nuclear science and engineering. Throughout this document the term, DOE National Laboratories, is used to collectively refer to DOE and NNSA National Laboratories, Sites, and Complexes. For DNN R&D, the role of Institutions of Higher Education (IHE; as defined in Section III.A. below) is to innovate, develop, and prove some of the most challenging basic aspects of new technology and methods in coordination with the DOE National Laboratories which can in turn fulfill their unique role to perform mission-specific research and development that improves on capabilities until they are either adopted by operational enterprises or transitioned into private industry for commercialization. Transparently and effectively linking the roles of these IHE and DOE National Laboratory represents the core of how DNN R&D proposes to meet its objectives. The intent of this Funding Opportunity Announcement (FOA) is to award ONE or TWO five-year cooperative agreement(s) to a consortium consisting of accredited IHE's to allow them to receive and administer funds for student and faculty research, fellowships, and scholarship funding awarded by DOE/NNSA, DNN R&D. The cooperative agreement will be awarded to a consortium of IHEs which will include the participation of DOE National Laboratories as a consortium-member(s). Individual consortium-member IHEs shall make specific contributions and shall receive specified portions of the funding. The consortium may include student and research fellows and must have a long-term objective of building expertise in nuclear science and engineering. Research results should be incorporated readily into IHE curricula. Students, faculty
The purpose of this funding opportunity announcement is to encourage collaborations between the life and physical sciences that: 1) apply a multidisciplinary bioengineering approach to the solution of a biomedical problem; and 2) integrate, optimize, validate, translate or otherwise accelerate the adoption of promising tools, methods and techniques for a specific research or clinical problem in basic, translational, or clinical science and practice. An application may propose design-directed, developmental, discovery-driven, or hypothesis-driven research and is appropriate for small teams applying an integrative approach to increase our understanding of and solve problems in biological, clinical or translational science.
NSWC Crane is interested in funding research of advanced 3D scene reconstruction techniques using imagery taken from currently fielded dynamic (moving) platforms. This proposed research is to assess and prototype methods currently utilized in commercial and academic systems to determine how to utilize several moving platforms in several different wavebands to reconstruct a 3D visualization of a scene.
We are seeking proposals for new research that will support and enhance the activities of the Consortium for the Valuation of Applications Benefits Linked to Earth Science (VALUABLES), a new consortium at RFF made possible through a partnership with the National Aeronautics and Space Administration (NASA). VALUABLES is focused on advancing innovative uses of existing methods and developing new techniques for valuing the information provided by Earth observations, especially those derived from satellites and aircraft.
Any area in which Earth observations play a role may be addressed, including applications relating to human health, air quality, water resources, ecosystem services, natural disasters, food security and agriculture, wildland fires, energy, urban development, and transportation and infrastructure. However, the use of remotely sensed data must be a key component of the analysis. In addition, we especially welcome proposals that focus on evaluating the socioeconomic impacts of applications of Earth observations for solving pressing societal problems and that quantify, in monetary terms, the value of Earth observations in specific applications. In so doing, proposals should clearly describe how information from Earth observations makes improvements to decision making and the value of those improvements. Applicants may propose to quantify the private and/or social benefits of applications of Earth observations, including nonmarket benefits.
The Macauley Award is open to researchers at US-based universities and nonprofit research institutions. Interdisciplinary research teams are preferred, and teams that involve both economists and Earth scientists are particularly encouraged.
The LEAP HI program challenges the engineering research community to take a leadership role in addressing demanding, urgent, and consequential challenges for advancing America's prosperity, health and infrastructure. LEAP HI proposals confront engineering problems that are too complex to yield to the efforts of a single investigator --- problems that require sustained and coordinated effort from interdisciplinary research teams, with goals that are not achievable through a series of smaller, short-term projects. LEAP HI projects perform fundamental research that may lead to disruptive technologies and methods, lay the foundation for new and strengthened industries, enable notable improvements in quality of life, or reimagine and revitalize the built environment.
The Institute for Research on Innovation and Science is accepting applications for its 2018 IRIS Awards, an annual program that supports researchers who use IRIS data to address questions about the social and economic returns of investments in research.
Through the program, IRIS seeks to enable fundamental research on the results of public and private investments that support discovery, innovation, and education on the campuses of U.S. universities. Up to $15,000 for dissertations awards and up to $30,000 for early career and established researcher awards will be awarded to the recipient's institution. Funds can be used for personnel (e.g., research assistance, salaries, or stipend if recipient is a student), equipment, supplies, travel (may include travel mandated by the award), and other expenses (e.g., professional development and training). Awards may include 15 percent overhead or indirect costs to be paid as a part of the award total.
Proposals must emphasize the use of IRIS data in projects that address open issues in the study of science and technology and science policy. Topics of particular interest include but are not limited to new methods to estimate social and economic return on investment for funding from various sources (federal, philanthropic, industrial, and institutional); the relationship between research training, career outcomes, and the downstream productivity of employers; the relationship between different funding sources and mechanisms and the structure and outcomes of collaboration within and across campuses; the distinctive contribution university research makes to regional economic development and resilience; and the effects different funding sources and mechanisms have on research teams and the productivity and efficiency of the academic research enterprise as a whole
With this DCL, ECR invites proposals on the development, application, and extension of formal models and methodologies for STEM education research and evaluation, including methods for improving statistical modeling, qualitative modeling, measurement, replication, and learning analytics. This includes research on methodological aspects of new or existing procedures for data collection, curation, and inference in STEM education. Similarly, ECR seeks proposals related to collection of unique databases with cross-boundary value, particularly when paired with innovative developments in measurement or methodology (standard statistical modeling, qualitative research, measurement, replication and learning analytics). Proposers must demonstrate how advances in the methodology will support important theoretical insights in STEM education research or evaluation. Proposers are encouraged to explore a wide range of fundamental research projects (in the areas of quantitative, qualitative, measurement, replication, and learning analytics methodologies)
The purpose of this FOA is to encourage submission of Exploratory/Developmental Bioengineering Research Grants (EBRG) applications which establish the feasibility of technologies, techniques or methods that: 1) explore a new multidisciplinary approach to a biomedical challenge; 2) are high-risk but have high impact; and 3) develop data that may lead to significant future research. An EBRG application may propose hypothesis-driven, discovery-driven, developmental, or design-directed research and is appropriate for evaluating unproven approaches for which there is minimal or no preliminary data.
NSWC Crane is interested in funding research of advanced 3D scene reconstruction techniques using imagery taken from currently fielded dynamic (moving) platforms. This proposed research is to assess and prototype methods currently utilized in commercial and academic systems to determine how to utilize several moving platforms in several different wavebands to reconstruct a 3D visualization of a scene.
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