Artificial intelligence and machine learning are hot topics in newly funded projects to address emerging scientific and technical challenges
NEWPORT NEWS, VA – From new particle accelerator technology, to the exploration of new ways to treat wastewater, to applications of artificial intelligence, six cutting-edge projects are getting a jumpstart on research and development at the Department of Energy’s Thomas Jefferson National Accelerator Facility.
The projects are supported by the Laboratory Directed Research and Development program, which recently announced the continuation of four projects and new funding for two more for fiscal year 2020, which began October 1. The LDRD program provides resources for Jefferson Lab personnel to make rapid and significant contributions to critical science and technology problems that further the goals of the laboratory and the DOE.
“We are delighted with the progress that was made on the ongoing LDRD projects, and we look forward with great interest to the results of the fiscal year 2020 projects and the boost they will give to long-term strategic directions of the laboratory,” said Jefferson Lab Director Stuart Henderson.
Of the six funded projects, four include aspects of artificial intelligence and machine learning: Three projects aim to develop machine learning to assist physicists in monitoring and/or analyzing large volumes of scientific data, while the last has the goal of improving up-time of Jefferson Lab’s Continuous Electron Beam Accelerator Facility, a DOE User Facility.
Information about the LDRD program and current projects can be found here.
The LDRD program supports small-scale research projects that expand on the lab’s core scientific capabilities. It allows for the conception and exploration of exciting opportunities toward proof-of-concept development of new scientific ideas, devices and facilities; advancement of new hypotheses; and innovative approaches toward scientific or technical problems.
Contact: Kandice Carter, Jefferson Lab Communications Office, 757-269-7263, email@example.com