coming soon
Given new experiments starting up and on the horizon, and the vastly increasing data volumes even at small experiments, the Nuclear Physics community has in recent years been thinking about the next generation of data processing and analysis workflows that will maximize the science output.
Thomas Jefferson National Accelerator Facility (Jefferson Lab) provides scientists worldwide the lab’s unique particle accelerator, known as the Continuous Electron Beam Accelerator Facility (CEBAF), to probe the most basic building blocks of matter by conducting research at the frontiers of nuclear physics (NP) and related disciplines. In addition, the lab capitalizes on its unique technologies and expertise to perform advanced computing and applied research with industry and university partners, and provides programs designed to help educate the next generation in science and technology.
Majority of computational science activities in Jefferson Lab focus on these areas : large scale and numerical intensive Lattice Quantum Chromodynamics (LQCD) calculations, modeling and simulation of accelerators and the experiment detectors, fast data acquisition and streaming data readout, high throughput computing for data analysis of experimental data, and large scale distributed data storage and management.
Many Jefferson Lab scientists and staffs lead or actively participate the computational efforts in the above areas. Among those are computer/computational scientists and computer professionals from newly formed computational sciences and technology division (CST), physicists from physics division and the Center for Theoretical and Computational Physics, and accelerator physicists from Center for Advanced Studies of Accelerators (CASA). In addition, collaborations with universities and industrial partners further research and development in computational science.
Jefferson Lab maintains various state of art high performance computing resources onsite. CSGF students will utilize these resources to carried out their researches in the specific areas described below:
CASA and Jefferson Lab SRF institute focus on advanced algorithms, such as fast multipole methods, for multiparticle accelerator dynamics simulations, artificial intelligence (AI) and machine learning (ML) applied to superconducting RF (SRF) accelerator operations, and integrated large and multi-scale modeling of SRF accelerator structures. These areas will be an essential part of a national strategy to optimize DOE operational facility investments, and to strengthen Jefferson Lab’s core competency of world-leading SRF advanced design and facility operations. Especially, current active simulation projects
like electron cooling, intra-beam scattering, and coherent synchrotron radiation present diverse research domains ranging from numerical algorithms development to parallel computing.
With tremendous advancement in micro-electronics and computing technologies in the last decade, many nuclear physics and high-energy physics experiments are taking advantage of these developments by upgrading their existing triggered data acquisition to a streaming readout model (SRO) , whereby detectors are continuously read out in parallel streams of data. An SRO system, which could handle up to 100 Gb/s data throughput, provides a pipelined data analysis model to nuclear physics experiments where data are analyzed and processed in near real-time fashion. Jefferson Lab is leading a collaborative research and development effort to devise SRO systems not only for CEBAF 12GeV experiments but also for the upcoming EIC facility. SRO development offers CSGF students some exciting research areas such as network protocol design, high speed data communication, high performance data compression and distributed computing.
Analysis of data from modern particle physics experiments uses technically advanced programming and computing techniques to handle the large volumes of data. One not only needs to understand aspects of parallel programming using modern languages such as C/C++, Java, and Python, but also must incorporate knowledge of experimental techniques involving error propagation and estimation in order to properly interpret the results. Aspects of this range from writing a single algorithm used in event reconstruction, to using the collection of algorithms written by others, to managing campaigns at HPC facilities that apply these algorithms to large datasets. Detector calibrations and final physics analysis are also significant parts of the analysis chain. CSGF students could participate in any of these areas.
Rapid developments in hardware computational power and an ever increasing set of data has lead to explosive growth in machine learning techniques, specifically deep learning techniques. These techniques threaten to change just about every facet of modern life and nuclear physics is no exception. At Jefferson Lab machine learning is being developed for every step in the physics workflow. To deliver beam to the experimental halls the accelerator relies on radio frequency (RF) cavities to accelerate the electrons. Occasionally these cavities, of which there are over 400 in operation around the accelerator, fault which disrupts the delivery of the beam to experiments. To quickly identify and diagnose cavity faults A.I. is being developed and deployed. Experiments themselves are developing and/or deploying A.I. to monitor detector performance, decide what data to keep, reconstruct detector responses, simulate the detectors, and even to analyze collected data. With the active development of machine learning tools and techniques Jefferson Lab hopes to drive nuclear physics research forward, enabling physicists to more quickly obtain and analyze high quality data.
Computational Sciences and Technology (CST) Division
The software computing round table meetings - we had one on Jupyter on Tuesday, December 3, 2019
In light of the evergrowing role that Software & Computing play in High Energy Physics, Nuclear Physics, and related fields, Brookhaven National Laboratory, the HEP Software Foundation, and Jefferson Lab are organizing the Software & Computing Round Table to foster the interplay of computing and science. The monthly round table forum aims for knowledge transfer and to encourage common projects within our scientific community.
Accelerate your career with a new role at the nation's newest national laboratory. Here you can be part of a team exploring the building blocks of matter and lay the ground work for scientific discoveries that will reshape our understanding of the atomic nucleus. Join a community with a common purpose of solving the most challenging scientific and engineering problems of our time.
A career at Jefferson Lab is more than a job. You will be part of “big science” and work alongside top scientists and engineers from around the world unlocking the secrets of our visible universe. Managed by Jefferson Science Associates, LLC; Thomas Jefferson National Accelerator Facility is entering an exciting period of mission growth and is seeking new team members ready to apply their skills and passion to have an impact. You could call it work, or you could call it a mission. We call it a challenge. We do things that will change the world.
At Jefferson Lab we believe in giving back to our community and encouraging the next generation of scientists and engineers. Our staff reaches out to students to advance awareness and appreciation of the range of research carried out within the DOE national laboratory system, to increase interest in STEM careers for women and minorities, and to encourage everyone to become a part of the next-generation STEM workforce. We are recognized for our innovative programs like:
1,500 students from 15 Title I schools engage in the Becoming Enthusiastic About Math and Science (BEAMS) program at the lab each school year.
60 teachers are enrolled in the Jefferson Science Associates Activities for Teachers (JSAT) program at the lab inspiring 9,000 students annually.
24 high school students have internships and 34 college students have mentorships at the lab.
Health physicist chairs inaugural DOE-wide artificial intelligence learning committee
Deputy Radiation Control Department Manager Adam Stavola knows how to learn. The health physicist at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility has spent all but eight years of his life in a formal academic program. So, when the U.S. Department of Energy was putting together an inter-lab task force to study and implement new artificial intelligence (AI) technologies, Stavola eagerly volunteered to be involved.
“I’m pursuing a Ph.D. in AI, so this new taskforce was very interesting to me,” he said.
The group formed in December 2022 and named Stavola as its chair.
“AI has advanced so much that leaders within the DOE determined that we need to learn about ways we can use AI in labs across the country,” Stavola explained. “The goal is for all the different laboratories within the DOE to pool together and try to solve specific problems using AI to help.”
Learning on the job
For the first project, the AI taskforce is focused on improving operational processes—due largely to the relatively low risk of applying AI in these situations.
“By choosing to incorporate AI with an operations project, our learning doesn’t have a direct safety impact,” Stavola said. “We’ve picked something for our first project that has a low potential for causing problems. If it works, great. If it doesn’t, we haven’t hurt anything.”
Stavola also notes that this project has the further benefit of opening lines of communication between loosely connected operations counterparts in labs across the country.
“Researchers across labs work together a lot, but operations teams don’t necessarily collaborate among the labs,” said Stavola. “We want to see how we can use AI to make operations more efficient by using data from all labs. Maybe AI can help us improve on best practices. Maybe it will enable us to make better predictions.”
Stavola also recognizes that AI may have a significant impact on efficiently processing data gleaned from laboratory operations.
“We are also trying to use AI to take care of things that would take humans too long,” he continued. “One of the things we do is look at our lessons learned. AI will mine the incredible amount of data we have about what has happened in the past in order to make predictions. So, if we are experiencing something, AI can notify us if we’ve previously been in a similar situation, and it can tell us what to expect.”
Stavola notes that AI is not being developed to work completely autonomously and without feedback from the human team.
“An example of how we might use AI, but not trust it fully, is: if AI detects potential for rain, we might grab an umbrella before we leave the house,” he explained. “Of course, we still check for rain when we step outside and before we open the umbrella.”
AI as more than a task force project
While Stavola is enthusiastic about his work with AI, he admits that his role on the AI task force represents only a small percentage of his responsibilities at the lab.
“My main job—and really what takes up most of my day and what I’ve been doing since 2006—is that I’m a board-certified health physicist,” Stavola shares. “As a health physicist for the lab, my work is all about radiation protection for laboratory staff, the environment and the general public.”
Stavola has a master’s degree in electrical and electronics engineering from Old Dominion University (ODU) and a master’s degree from Christopher Newport University in applied physics and computer science. Currently, he is a Ph.D. candidate at ODU researching how AI can use meteorological data to predict relative uranium concentrations in soil.
Like the AI learning taking place at the lab, Stavola says he is invested in his own unending process of intentional learning, which he views as his biggest hobby.
“You get interested in all these things and you go down all these different pathways,” he laughed. “Some would say I’ve been a glutton for punishment; I’ve been in school my entire adult life. I’m constantly trying to get better at what I’m doing. It’s about pushing myself. And, yes, learning is my biggest hobby. Otherwise, this much school doesn’t make any sense!”
“I’ve learned that the more you learn, the more you realize you don’t know,” he said, recognizing that it’s a bit cliché. “The more you learn, the more you want to learn. I am constantly discovering things that I don’t know about, but I want to know about. And you learn about more niche subjects and say, ‘Oh this is new. Lemme go dig into this for a little while.’ You always want to push it a little more. I think most people are like that.”
Further Reading:
Radiation Control Department
Jefferson Lab Accelerator Operations
By Carrie Rogers
The Jefferson Lab campus is located in southeastern Virginia amidst a vibrant and growing technology community with deep historical roots that date back to the founding of our nation. Staff members can live on or near the waterways of the Chesapeake Bay region or find peace in the deeply wooded coastal plain. You will have easy access to nearby beaches, mountains, and all major metropolitan centers along the United States east coast.
To learn more about the region and its museums, wineries, parks, zoos and more, visit the Virginia tourism page, Virginia is for Lovers.
To learn more about life at Jefferson Lab, click here.
We support our inventors! The lab provides resources to employees for the development of patented technology -- with over 180 awarded to date! Those looking to obtain patent coverage for their newly developed technologies and inventions while working at the lab are supported and mentored by technology experts, from its discovery to its applied commercialization, including opportunities for monetary awards and royalty sharing. Learn more about our patents and technologies here.
"Not every day is the same day. Working in research and development, it’s not a one person job."
"My own research enables me to better lead the Theory Center, to lead our collaboration, to provide good guidance to our junior researchers on the team, and to provide valuable input to the advisory and review committees that I serve"
“Generally, the mechanical engineers at the lab support the physicists. The physicists have the big ideas about how to support new science, and the engineers figure out how to make that happen.”
"When I’m 95 years old, I hope I will be one of those people who worked in the background to affect other people’s lives for the better."
“Chemistry is the art of science and art; you’re manipulating and creating things. We have lots of different recipes to work with.”
Jefferson Science Associates, LLC manages and operates the Thomas Jefferson National Accelerator Facility. Jefferson Science Associates/Jefferson Lab is an Equal Opportunity and Affirmative Action Employer and does not discriminate in hiring or employment on the basis of race, color, religion, ethnicity, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, or veteran status or on any other basis prohibited by federal, state, or local law.
If you need a reasonable accommodation for any part of the employment process, please send an e-mail to recruiting @jlab.org or call (757) 269-7100 between 8 am – 5 pm EST to provide the nature of your request.
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A Proud V3-Certified Company
JSA/Jefferson Lab values the skills, experience and expertise veterans can offer due to the myriad of experiences, skill sets and knowledge service members achieve during their years of service. The organization is committed to recruiting, hiring, training and retaining veterans, and its ongoing efforts has earned JSA/Jefferson Lab the Virginia Values Veterans (V3) certification, awarded by the Commonwealth of Virginia.
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Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock,
