RSS feed source: National Science Foundation

The U.S. National Science Foundation announced a partnership with NVIDIA to develop a set of artificial intelligence models that will transform the ability of America’s scientists to leverage AI, advancing scientific discovery and ensuring U.S. leadership in AI-powered research and innovation. NSF will contribute $75 million, with NVIDIA providing an additional $77 million, to support the Open Multimodal AI Infrastructure to Accelerate Science (OMAI) project, led by the Allen Institute for AI (Ai2). The collaboration will create a fully open suite of advanced AI models specifically designed to support the U.S. scientific community.

This public-private investment advances priorities set forth in the White House AI Action Plan to accelerate AI-enabled science and ensure the United States is producing the leading open models that enhance America’s global AI dominance.

“Bringing AI into scientific research has been a game changer,” said Brian Stone, performing the duties of the NSF director. “NSF is proud to partner with NVIDIA to equip America’s scientists with the tools to accelerate breakthroughs. These investments are not just about enabling innovation; they are about securing U.S. global leadership in science and technology and tackling challenges once thought impossible.”

The development of AI technologies is advancing rapidly, but the cost of creating and researching powerful AI models has grown beyond the budgets of university labs and federally funded researchers. This growing divide limits the

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RSS feed source: National Science Foundation

Research Opportunities

Robotics

Work description

Development of a low-cost system for testing lithium-ion battery cells, using identification techniques to determine charge and discharge models. These models will be fundamental for the design of battery management systems (BMS) in autonomous maritime vehicles, such as submarines and boats.

The system will allow accurate estimation of the state of charge (SOC) and state of health (SOH) of the batteries, optimizing mission planning and allowing the scheduled replacement of cells without interruptions in the operation of the vehicles. Writing of the Grant reports.

Academic Qualifications

Ongoing higher education training in the area of Electrical and Computer Engineering.

Minimum profile required

Advanced knowledge of Python, C and C++. Experience in microcontroller design and hardware development. Proven experience and knowledge in the field of lithium batteries. Proven ability to work autonomously and proactively.

Preference factors

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Research Opportunities

Informatics, electrical and electronic, and biomedical Engineering

Work description

Extend the knowledge of the state of the art in machine learning for generation of artificial data. Identify and select the appropriate methods for the study in question. Develop the research capacity through the application of the selected methods. Exercise a critical spirit in the evaluation of the research process and the results obtained.

Minimum profile required

Experience in Computer Vision and machine learning.

Preference factors

Experience in research projects, and writing of scientific papers.

Maintenance stipend: € 1309.64, according to the table of monthly maintenance stipend for FCT grants , paid via bank transfer. Grant holders may be awarded potential supplements, according to a quarterly evaluation process (Articles 19, 21 and 22 of the Regulations for Grants of INESC TEC and Annex II), up to a maximum

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Research Opportunities

Engineering Systems; Logistics

Work description

Case study analysis and requirements. Application of a control tower tool in a real industrial context. Publication of a scientific document. Writing the scholarship activity report.

Academic Qualifications

Master’s degree in engineering and industrial management. Experience in the real-world application of LLM technologies, ontologies and graph databases in logistics and supply chain management contexts. Fluent in Portuguese.

Minimum profile required

Master’s degree in engineering and industrial management. Knowledge of supply chain management and logistics. Ability to analyse and understand case studies. Experience in the practical application of LLM technologies, ontologies and graph databases in logistics and supply chain management contexts. Fluent in Portuguese;

Preference factors

Experience in Python programming. Experience using Neo4j. Experience using LangChain. Experience using Protégé for ontology creation and management. Experience with logistics analytics tools.

Maintenance

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