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This call, issued to universities worldwide, may be addressed by an individual investigator or a research team. Our selection process is divided into two stages. Interested parties are requested to submit a 1-page white paper, which should identify what can be done in a three-year period beginning January 1, 2019. A successfully selected white paper will result in an invitation to submit a full proposal. These proposals will be further down-selected for research contracts.

Global research and development at Sony enables us to foster innovative ideas, which could ultimately lead to future technology advancements and company growth. In order to speed up and expand the creation of new ideas, we would like to partner with universities. This partnership will help cultivate advanced concepts and fertilize our own research and development. The Sony Faculty Innovation Award provides up to $100K in funds to conduct pioneering research in the areas of visualization; computer vision; machine learning; robotics; communications and networking; RF sensing; audio; speech and natural language processing; human computer interaction; mobility; system software; and LSI and hardware.

EXplainable Artificial Intelligence (XAI) aims to provide strong predictive value along with mechanistic understanding by combining machine learning techniques with effective explanatory techniques. This Funding Opportunity Announcement (FOA) solicits applications in the area of XAI applied to neuroscientific questions of encoding, decoding, and modulation of neural circuits linked to behavior. This FOA encourages collaborations between computationally and experimentally-focused investigators. This FOA seeks machine learning algorithms able to mechanistically explain how experimental manipulations can improve cognitive, affective, or social processing in humans or animals. Proof-of-concept applications aimed at improving the current state of the technology that use XAI to provide unbiased, hierarchical explanations of causal relationships between complex neural and behavioral data are also responsive.

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (see https://www.nsf.gov/news/special_reports/big_ideas/index.jsp). One of these ideas, "The Quantum Leap: Leading the Next Quantum Revolution," advances quantum technologies of the future: quantum computing, quantum communication, quantum simulations and quantum sensors. Recent advances in understanding and exploiting quantum mechanics are laying the foundation for generations of new discoveries that can benefit society in unforeseen ways. This "quantum revolution" requires a highly-trained workforce that can advance the envelope of what is possible, through research and development of practical solutions for quantum technologies. Academic faculty serve a vital role in the development of this workforce, by training the next generation of students while performing vital research. The disciplines of computer science (CS), information science (IS), and computer engineering (CE) are at the nexus of the interdisciplinary breakthroughs needed to design advanced quantum computing, modeling, communication and sensing technologies. NSF recognizes that there is inadequate research capacity in the CS/CE disciplines in the realm of Quantum Computing & Information Science (QCIS).

Transdisciplinary Research In Principles Of Data Science (TRIPODS) aims to bring together the statistics, mathematics, and theoretical computer science communities to develop the theoretical foundations of data science through integrated research and training activities. Phase I, described in solicitation NSF 16-615, supported the development of small collaborative Institutes. Phase II will support a smaller number of larger Institutes, selected from the Phase I Institutes via a second competitive proposal process. All TRIPODS Institutes must involve significant and integral participation by all three of the aforementioned communities.

The National Science Foundation (NSF) Directorates for Engineering (ENG) and Geosciences (GEO), the Divisions of Integrative Organismal Systems (IOS) and Environmental Biology (DEB), in the Directorate for Biological Sciences (BIO), the Division of Computer and Network Systems in the Directorate Computer and Information Science and Engineering (CISE/CNS), and the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences, in collaboration with the US Department of Agriculture National Institute of Food and Agriculture (USDA NIFA) encourage convergent research that transforms existing capabilities in understanding dynamic soil processes, including soil formation, through advances in sensor systems and modeling. The Signals in the Soil (SitS) program fosters collaboration among the two partner agencies and the researchers they support by combining resources and funding for the most innovative and high-impact projects that address their respective missions. To make transformative advances in our understanding of soils, multiple disciplines must converge to produce environmentally-benign novel sensing systems with multiple modalities that can adapt to different environments and collect and transmit data for a wide range of biological, chemical, and physical parameters. Effective integration of sensor data will be key for achieving a better understanding of signaling interactions among plants, animals, microbes, the soil matrix, and aqueous and gaseous components. New sensor networks have the potential to inform models in novel ways, to radically change how data is obtained from various natural and managed (both urban and rural) ecosystems, and to better inform the communities that directly rely on soils for sustenance and livelihood.

In light of the emergence and spread of the coronavirus disease 2019 (COVID-19) in the United States and abroad, the National Science Foundation (NSF) is accepting proposals to conduct non-medical, non-clinical-care research that can be used immediately to explore how to model and understand the spread of COVID-19, to inform and educate about the science of virus transmission and prevention, and to encourage the development of processes and actions to address this global challenge. NSF encourages the research community to respond to this challenge through existing funding opportunities. In addition, we invite researchers to use the Rapid Response Research (RAPID) funding mechanism, which allows NSF to receive and review proposals having a severe urgency with regard to availability of or access to data, facilities or specialized equipment as well as quick-response research on natural or anthropogenic disasters and similar unanticipated events. Requests for RAPID proposals may be for up to $200K and up to one year in duration. Well-justified proposals that exceed these limits may be entertained.

The Algorithms for Threat Detection (ATD) program supports research on new ways to use spatiotemporal datasets to develop quantitative models of human dynamics. The objectives include improved representation of complicated group dynamics and the development of algorithms that can process data in near real-time to accurately identify unusual events and forecast future threats indicated by those events.

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.

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.

Quantum computing is a revolutionary approach to information processing based on the quantum physics of coherent superposition and entanglement.  Advantages of quantum computing include efficient algorithms for computationally difficult tasks, efficient use of resources such as memory and energy needed for computations, and new platforms for the simulation of quantum mechanical systems that are currently intractable using conventional computers.  Applications for quantum computing, such as integer number factoring, search and optimization algorithms, and quantum simulations, will accelerate discoveries in a broad range of disciplines including physics, engineering, and computer science.

This funding opportunity announcement (FOA) encourages grant applications for national Biomedical Technology Research Resources (BTRR). These Resources conduct research and development of new or improved technologies driven by the needs of basic, translational, and clinical researchers. The Resources are charged to make their technologies available to the research community in a sustainable manner, to provide user training, and to disseminate the Resources technologies and experimental results. Resources should be at the leading edge of their field with respect to both technology development and engagement of the relevant research community. New applicants are strongly encouraged to submit a pre-application in response to PAR-17-315 The pre-application process provides feedback on whether the proposed technology development is appropriate for the NIGMS BTRR program, and the potential competitiveness of a full application.

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. On September 3, 1992, DOE published in the Federal Register the Office of Energy Research Financial Assistance Program (now called the Office of Science Financial Assistance Program), 10 CFR 605, as a Final Rule, which contained a solicitation for this program. Information about submission of applications, eligibility, limitations, evaluation and selection processes and other policies and procedures are specified in 10 CFR 605.

This Funding Opportunity Announcement (FOA) solicits grant applications proposing exploratory research projects focused on the early-stage development of highly innovative technologies that improve the quality of the samples used for cancer research or clinical care. This includes new capabilities to address issues related to pre-analytical degradation of targeted analytes during the collection, processing, handling, and/or storage of cancer-relevant biospecimens. The overall goal is to support the development of highly innovative technologies capable of maximizing or otherwise interrogating the quality and utility of biological samples used for downstream analyses. This FOA will support the development of tools, devices, instrumentation, and associated methods to preserve or protect sample integrity, or establish verification criteria for quality assessment/quality control and handling under diverse conditions. These technologies are expected to accelerate and/or enhance research in cancer biology, early detection and screening, clinical diagnosis, treatment, epidemiology, or address issues associated with cancer health disparities, by reducing pre-analytical variations that affect biospecimen sample quality.

The L'Oréal USA For Women in Science fellowship program awards five women postdoctoral scientists annually with grants of $60,000 each for their contributions in Science, Technology, Engineering and Math (STEM) fields and commitment to serving as role models for younger generations. The program is the U.S. component of the L'Oréal-UNESCO For Women in Science International Fellowships program. L'Oréal USA partners with the American Association for the Advancement of Science (AAAS) to manage the program's application and peer-review process. The 2018 L'Oréal USA for Women in Science application period will open December 4th, 2017 and will close on February 2nd, 2018.

Secretary of the Air Force Heather Wilson announced in September 2017 inception of the "S&T Strategy 2030" study. The study's objective is to update the Air Force methods for conducting research and development (R&D) to meet the projected national security challenges of 2030. Secretary Wilson appointed the Air Force Research Laboratory (AFRL) to lead this strategy development.The Air Force's goals for the study are: 1. Evaluate technical approaches and focus areas to advance the Air Force's mission through R&D; and 2. Improve Air Force processes and organizational structures to manage early stage research.

The agency wishes to enter into one or more contracts that prequalifies selected offerors to establish a pool of pre-qualified Contractors that offer public/cloud based analytics platform processing capabilities and to provision for a State Public, Private and Protected data analytics platform suitable for conducting projects very large dataset analysis using a combination of open-source, commercial and vendor proprietary computing and storage platforms including highly complex numerical analysis of textual, structured, non-structured, spatial and other data sources. The agency will also establish a pool of pre-qualified Contractors that are capable of performing Exploratory Projects in a multitude of Data Analytics Domains based upon State agency need.

NOTE: This is a limited submission opportunity. Please contact Research & Sponsored Programs for details about Miami's internal competition process. OARS@MiamiOH.edu or 9-3600. The Major Research Instrumentation (MRI) Program serves to increase access to multi-user scientific and engineering instrumentation for research and research training in our Nation's institutions of higher education and not-for-profit scientific/engineering research organizations. An MRI award supports the acquisition or development of a multi-user research instrument that is, in general, too costly and/or not appropriate for support through other NSF programs. MRI provides support to acquire critical research instrumentation without which advances in fundamental science and engineering research may not otherwise occur. MRI also provides support to develop next-generation research instruments that open new opportunities to advance the frontiers in science and engineering research. Additionally, an MRI award is expected to enhance research training of students who will become the next generation of instrument users, designers and builders. An MRI proposal may request up to $4 million for either acquisition or development of a research instrument. Beginning with the FY 2018 competition, each performing organization may submit in revised "Tracks" as defined below, with no more than two submissions in Track 1 and no more than one submission in Track 2. Track 1: Track 1 MRI proposals are those that request funds from NSF greater than or equal to $100,0001 and less than $1,000,000. Track 2: Track 2 MRI proposals are those that request funds from NSF greater than or equal to $1,000,000 up to and including $4,000,000.

NSF-supported science and engineering research increasingly relies on cutting-edge infrastructure. With its Major Research Instrumentation (MRI) program and Major Multi-user Facilities (Major Facilities) projects, NSF supports infrastructure projects at the lower and higher ends of infrastructure scales across science and engineering research disciplines. The Mid-scale Research Infrastructure Big Idea is intended to provide NSF with an agile, Foundation-wide process to fund experimental research capabilities in the mid-scale range between the MRI and Major Facilities thresholds. NSF defines Research Infrastructure (RI) as any combination of facilities, equipment, instrumentation, or computational hardware or software, and the necessary human capital in support of the same. Major facilities and mid-scale projects are subsets of research infrastructure. The NSF Mid-scale Research Infrastructure-1 Program (Mid-scale RI-1) supports the design or implementation of unique and compelling RI projects. Mid-scale RI-1 implementation projects may include any combination of equipment, instrumentation, cyberinfrastructure, broadly used large-scale datasets, and the commissioning and/or personnel needed to successfully complete the project, or the design efforts intended to lead to eventual implementation of a mid-scale class project. Mid-scale RI-1 design projects will include the design efforts intended to lead to eventual implementation of a mid-scale class RI project. Mid-scale RI-1 projects should fill a research community-defined scientific need or enable a national research priority to be met. Mid-scale RI-projects should also enable US researchers to remain competitive in a global research environment and involve the training of a diverse workforce engaged in the design and implementation of STEM infrastructure.