RSS feed source: National Science Foundation

U.S. National Science Foundation-supported research shows that fires in populated areas are three times more likely to lead to premature deaths than wildfires overall, informing fire mitigation efforts.

Scientists at the NSF National Center for Atmospheric Research (NSF NCAR) led the study, published in Science Advances, which found that smoke from fires that blaze through the wildland-urban interface (WUI) has far greater health impacts than smoke from wildfires in remote areas.

“This research will support the development of advanced fire prevention strategies, improve building codes and lead to effective emergency response plans,” said Bernard Grant, a program director in the NSF Directorate for Geosciences. “It will help protect lives and homes, safeguard natural ecosystems and reduce the economic burden of wildfire disasters,”

The researchers used an advanced NSF NCAR-based computer model, the Multi-Scale Infrastructure for Chemistry and Aerosols, to simulate pollutants from fires. Their modeling included carbon monoxide chemical tracers, which allowed them to estimate emission sources and differentiate between wildland and WUI fires.

“The health impacts are proportionately large because they’re close to human populations,” said NSF NCAR scientist Wenfu Tang, the report’s lead author. “Pollutants emitted by WUI fires, such as particulate matter and the precursors to ozone, are more harmful because they’re not dispersing across hundreds or thousands of miles.”

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RSS feed source: National Science Foundation

Job ID: 256553

INESC TEC | Research Grant (AE2025-0187)
INESC TEC

Research Opportunities

Engineering

Work description

Study and implement a distributed architecture to support the execution of MLLMs. Propose solutions to evaluate trade-offs between computation, communication, and latency. Study the state of the art in MLLMs applicable to the task of analyzing industrial machine operations. Develop and optimize the computer vision pipeline. Create a performance evaluation system for the models. Document the developed solutions.

Academic Qualifications

Degree in Informatics, Electrical and Computer Engineering, Computer Science or similar.

Minimum profile required

Programming experience (Python, Go, others).

Preference factors

Experience in machine learning models, image processing, computer vision or machine learning, and particularly MLLMs, OR Experience in distributed systems and distributed workload management systems. Previous research experience.

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RSS feed source: National Science Foundation

Job ID: 256554

INESC TEC | Research Initiation Grant (AE2025-0193)
INESC TEC

Research Opportunities

Industrial Robotics

Work description

Prepare a comprehensive literature review on robotic grasping and binpicking techniques with exclusive use of machine learning and intelligence artificial. Develop a grasping pipeline integrated with ROS, aimed at tasks of bin-picking with support for AI algorithms. Acquire practical skills in using the Omniverse simulator, focusing on simulation of robots and evaluation of grasping algorithms. Perform experimental tests in a simulated environment and with a physical robot, whenever possible. Write the scholarship activity report, documenting developments and results achieved. Contribute to projects that require grasping activities in robots. Prepare and submit a scientific paper to a relevant conference or journal in the robotics or AI area.

Academic Qualifications

Bachelor’s Degree in

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