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The Mind, Machine and Motor Nexus (M3X) program supports fundamental research at the intersection of mind, machine and motor. A distinguishing characteristic of the program is an integrated treatment of human intent, perception, and behavior in interaction with embodied and intelligent engineered systems and as mediated by motor manipulation. M3X projects should advance the holistic analysis of cognition and of embodiment as present in both human and machine elements. This work will encompass not only how mind interacts with motor function in the manipulation of machines, but also how, in turn, machine response and function may shape and influence both mind and motor function.

The Mind, Machine and Motor Nexus (M3X) program supports fundamental research at the intersection of mind, machine and motor. A distinguishing characteristic of the program is an integrated treatment of human intent, perception, and behavior in interaction with embodied and intelligent engineered systems and as mediated by motor manipulation. M3X projects should advance the holistic analysis of cognition and of embodiment as present in both human and machine elements. This work will encompass not only how mind interacts with motor function in the manipulation of machines, but also how, in turn, machine response and function may shape and influence both mind and motor function.

The Mind, Machine and Motor Nexus (M3X) program supports fundamental research at the intersection of mind, machine and motor. A distinguishing characteristic of the program is an integrated treatment of human intent, perception, and behavior in interaction with embodied and intelligent engineered systems and as mediated by motor manipulation. M3X projects should advance the holistic analysis of cognition and of embodiment as present in both human and machine elements. This work will encompass not only how mind interacts with motor function in the manipulation of machines, but also how, in turn, machine response and function may shape and influence both mind and motor function.

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 MME program supports fundamental research leading to improved manufacturing machines and equipment, and their application in manufacturing processes.  Key goals of the program are to advance the transition of manufacturing from skill-based to knowledge-based activities, and to advance technologies that will enable the manufacturing sector to reduce its environmental and societal impacts.  A focus is on the advancement of manufacturing machines and related systems engineering that will enable energy manufacturing on a large scale.  The program also supports research on additive manufacturing machines and processes encompassing feature scales from microns to meters (nanometer scale additive manufacturing is supported under the Nanomanufacturing program).

The MME program supports fundamental research leading to improved manufacturing machines and equipment, and their application in manufacturing processes.  Key goals of the program are to advance the transition of manufacturing from skill-based to knowledge-based activities, and to advance technologies that will enable the manufacturing sector to reduce its environmental impacts.  A focus is on the advancement of manufacturing machines and related systems engineering that will enable energy manufacturing, namely the manufacture of facilities and equipment that will enable the conversion of renewable resources into energy products such as electricity and liquid fuels, on a large scale.  The program also supports research on laser processing, joining processes and additive manufacturing machines and processes encompassing feature scales from microns to meters (nanometer scale additive manufacturing is supported under the Nanomanufacturing program).

The HFC program seeks proposals for research to develop and test hybrid geopolitical forecasting systems. These systems will integrate human and machine forecasting components to create maximally accurate, flexible, and scalable forecasting capabilities. Human-generated forecasts may be subject to cognitive biases and/or scalability limits. Machine-generated (i.e., statistical, computational) forecasting approaches may be more scalable and data-driven, but are often ill-suited to render forecasts for idiosyncratic or newly emerging geopolitical issues. Hybrid approaches hold promise for combining the strengths of these two approaches while mitigating their individual weaknesses. Performers will develop systems that will integrate human and machine forecasting contributions in novel ways. These systems will compete in a multi-year competition to identify approaches that may enable the Intelligence Community (IC) to radically improve the accuracy and timeliness of geopolitical forecasts.

The MME program supports fundamental research leading to improved manufacturing machines and equipment, and their application in manufacturing processes. Key goals of the program are to advance the transition of manufacturing from skill-based to knowledge-based activities, and to advance technologies that will enable the manufacturing sector to reduce its environmental impacts. A focus is on the advancement of manufacturing machines and related systems engineering that will enable energy manufacturing, namely the manufacture of facilities and equipment that will enable the conversion of renewable resources into energy products such as electricity and liquid fuels, on a large scale. The program also supports research on laser processing, joining processes and additive manufacturing machines and processes encompassing feature scales from microns to meters 

The Dreyfus program for Machine Learning in the Chemical Sciences and Engineering, initiated in 2020, provides funding for innovative projects in any area of Machine Learning (ML) consistent with the Foundation's broad objective to advance the chemical sciences and engineering. The Foundation anticipates that these projects will contribute new fundamental chemical insight and innovation in the field.

The MME program supports fundamental research leading to improved manufacturing machines and equipment, and their application in manufacturing processes. Key goals of the program are to advance the transition of manufacturing from skill-based to knowledge-based activities, and to advance technologies that will enable the manufacturing sector to reduce its environmental impacts. A focus is on the advancement of manufacturing machines and related systems engineering that will enable energy manufacturing, namely the manufacture of facilities and equipment that will enable the conversion of renewable resources into energy products such as electricity and liquid fuels, on a large scale. The program also supports research on additive manufacturing, laser processing and bonding/joining processes encompassing feature scales from microns to meters. Proposals with focus on materials for these processes are also welcome in MME. Note: nanometer scale additive manufacturing is supported under the Nanomanufacturing program.Investigators wishing to serve on a proposal review panel should email the Program Director with a short biographical sketch, a list of areas of expertise and a link to their home page. REU/RET supplement requests should be submitted by March 31 each year.

The Advanced Manufacturing (AM) program supports the fundamental research needed to revitalize American manufacturing to grow the national prosperity and workforce, and to reshape our strategic industries. The AM program accelerates advances in manufacturing technologies with emphasis on multidisciplinary research that fundamentally alters and transforms manufacturing capabilities, methods and practices. Advanced manufacturing research proposals should address issues related to national prosperity and security, and advancing knowledge to sustain global leadership. Areas of research, for example, include manufacturing systems; materials processing; manufacturing machines; methodologies; and manufacturing across the length scales. Researchers working in the areas of cybermanufacturing systems, manufacturing machines and equipment, materials engineering and processing, and nanomanufacturing are encouraged to transcend and cross domain boundaries. Interdisciplinary, convergent proposals are welcome that bring manufacturing to new application areas, and that incorporate challenges and approaches outside the customary manufacturing portfolio to broaden the impact of America's advanced manufacturing research. Proposals of all sizes will therefore be considered as justified by the project description. Investigators are encouraged to discuss their ideas with AM program directors well in advance of submission at AdvancedManufacturing@nsf.gov.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications, healthcare informatics and business analytics.

The goal of the RF Machine Learning Systems (RFMLS) program is to develop the foundations for applying modern data-driven Machine Learning to the RF Spectrum domain as well as to develop practical applications in emerging spectrum problems, which demand vastly improved discrimination performance over today's hand-engineered RF systems. Ultimately, these innovations will result in a new generation of RF systems that are goal-driven and can learn from data rather than being hand-engineered by experts.

The research opportunity described in this announcement falls under the following sections of the BAA: Appendix 1 "Program Description," * Section I entitled "Expeditionary Maneuver Warfare & Combating Terrorism (Code 30); specific thrusts and focused research area: * Paragraph E. "ONR 30 Decision Support, AI, Machine Learning and Graph Analysis Program," Technology Investment area 3, entitled "Modeling/ Machine Learning/ /Artificial Intelligence" *

The Air Force Office of Scientific Research (AFOSR) seeks unclassified proposals from educational institutions in the United States for a University Center of Excellence (UCoE) in in Trusted Human-Machine Teaming. Proposals must not contain any proprietary information. This center is a joint project between the Air Force Office of Scientific Research and the Air Force Research Laboratory, Airman Systems Directorate (AFRL/RH), referred to collectively as "we, our, or us" in this announcement. The center will extend the research capabilities of the Air Force Research Laboratory, and provide opportunities for a new generation of United States scientists and engineers to address the basic research needs of the Air Force. We will consider proposals for up to five (5) years with a three-year (3) base period and a two-year (2) option period.

This mechanism supports basic research to increase knowledge/understanding through discovery and hypothesis generation, and should focus on providing basic fundamental knowledge that will inform and enable the future development of novel autonomous and/or robotic medical systems to care for wounded soldiers/patients through breakthrough, exploratory research. The objective is focused on addressing the following 1. Autonomous and Unmanned Medical Capability - Identify novel ideas, approaches and research towards the conceptualization of autonomous and unmanned technologies for next-generation, high-quality medical capabilities with limited or absent medical care personnel, or personnel with limited skills. Research novel concepts, plausible approaches and advanced concept designs using biologically inspired cognitive computing models, machine learning, artificial intelligence, soft robotic semi-autonomous/autonomous resuscitation concepts and advanced applications of information sciences among other innovative, exploratory research towards advancing the state-of-the-art in delivery of forward resuscitative care at the point of injury. 2. Medical Robotics Research - Identify novel ideas, approaches and research towards the conceptualization of medical robotics and real-time tele-presence capabilities exploring the limits of machine perception for tele-robotic semi-autonomous and autonomous trauma care within remote and dispersed geographic settings. This could include exploratory research in semi-autonomous robotic surgery to improve the safety profile and efficacy of tele-surgical procedures and outcomes using hard robotics in challenging situations (e.g., combat casualties on the multi-domain battlefield or mass casualty situations) and remote or austere geographic locations, among other innovative, exploratory research aims and novel concepts.

The primary objectives of the Reversible/Quantum Machine Learning and Simulation (RQMLS) AIE opportunity are (1) to explore the fundamental limits of reversible quantum annealers; (2) to quantitatively predict the computational utility of these systems for machine learning, simulation, and other important tasks; and (3) to design experimental tests for these predictions that can be carried out on newly fabricated small-scale reversible quantum annealers.

The Spectrum Collaboration Challenge will develop intelligent radio networks which can collaborate to manage and optimize the radio frequency (RF) spectrum in a complex dynamic, changing RF environment which consists of other collaborative radio networks, non-collaborative radio networks (which are incapable of adapting) and other potential interference sources. Successful networks will apply machine learning techniques and be able to optimize total spectrum usage by determining when, where and how to utilize its resources. The networks for all participant teams will be evaluated in a series of competitive, tournament-style events.

The Spectrum Collaboration Challenge will develop intelligent radio networks which can collaborate to manage and optimize the radio frequency (RF) spectrum in a complex dynamic, changing RF environment which consists of other collaborative radio networks, non-collaborative radio networks (which are incapable of adapting) and other potential interference sources. Successful networks will apply machine learning techniques and be able to optimize total spectrum usage by determining when, where and how to utilize its resources. The networks for all participant teams will be evaluated in a series of competitive, tournament-style events.

The MME program supports fundamental research that enables the development of new and/or improved manufacturing machines and equipment, and optimization of their use, with a particular focus on equipment appropriate for the manufacture of mechanical and electromechanical devices, products, and systems featuring scales from microns to meters (proposals relating to nanomanufacturing should be submitted to the CMMI NanoManufacturing program, and those relating to the manufacture of electronic devices such as IC products should be submitted to the ECCS Division). 

The NSF 2026 Idea Machine is a competition to help set the U.S. agenda for fundamental research in science and engineering. Participants can earn prizes and receive public recognition by suggesting the pressing research questions that need to be answered in the coming decade, the next set of "Big Ideas" for future investment by the National Science Foundation (NSF). It's an opportunity for researchers, the public and other interested stakeholders to contribute to NSF's mission to support basic research and enable new discoveries that drive the U.S. economy, enhance national security and advance knowledge to sustain the country's global leadership in science and engineering.