Student AssistantJob ID 2770 Date posted 08/17/2021
Brookhaven National Laboratory (www.bnl.gov) delivers discovery science and transformative technology to power and secure the nation’s future. Brookhaven Lab is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and more than 70 years of pioneering research. The Lab is primarily supported by the U.S. Department of Energy’s (DOE) Office of Science. Brookhaven Science Associates (BSA) operates and manages the Laboratory for DOE. BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University.
Brookhaven National Laboratory is entering an exciting new chapter with one of the newest and most advanced synchrotron facilities in the world. National Synchrotron Light Source II (NSLS-II ) enables the study of material properties and functions with nanoscale resolution and exquisite sensitivity by providing world-leading capabilities for X-ray imaging and high-resolution energy analysis. This facility is open to users from academia and industry, and its operations are at a time when the world enters a new era with a global economy fueled largely by scientific discoveries and technological innovations. NSLS-II provides the research tools needed to foster new discoveries and create breakthroughs in critical areas such as energy security, environment, and human health.
The Computational Science Initiative (CSI) provides a laboratory-wide umbrella for design, planning, analysis, and interpretation of experiments and their results, bringing together computer scientists, applied mathematicians, and domain scientists to carry out leading-edge research, convert research results into practical solutions that advance domain science, and provide the necessary computing infrastructure services and training to support efficient operation.
X-ray Photon Correlation Spectroscopy (XPCS) is one of the techniques at NSLS-II that uses a coherent X-ray beam to study nanoscale dynamics of a variety of systems. With ever increasing data acquisition rates, the demand for data processing is becoming hard to be met by researchers, especially during their experiment. Given that competitively awarded experiments occur during a 3-4 day period every 4 months, it is critical to have highly-reliable “real-time” analysis to make decisions during the experiment. Our team (collaboration between NSLS-II and CSI researchers) is using Machine Learning for developing the tools for automatic data analysis of coherent X-ray scattering experiments that will allow for more efficient use of experimental resources and acceleration of scientific discoveries.
We are looking for a Student Assistant who will join the ongoing team efforts in developing the tools that will ensure data quality and provide on-the-fly insights about material dynamics. The successful candidate will be involved in both development of Machine Leaning models and their deployment to the beamline operations. The position involves working closely with other group members: scientists and postdocs.
Student assistants are restricted to <19hr/week during the semester and may work for longer periods during the official university breaks.
Essential Duties and Responsibilities:
- Process, select, organize and analyze the X-ray scattering data for model training
- Assist/work independently on model development
- Setting up model testing for identifying its applicability for a broad range of samples and experimental conditions
- Write code for integrating the models into traditional XPCS data collection and analysis pipelines
- Report the results in the form of scientific publications and/or software documentation
Required Knowledge, Skills, and Abilities
- Enrolled in a MS program or 4th year Bachelors program concentrating in Computer Science, Applied Mathematics, Engineering, Physics or similar
- Permission of academic advisor, if pursuing a researched-based and not a coursework-based Masters degree
- Python programming experience
- Familiarity with at least one of the following: statistics, signal processing, development of computational work-flows (scalable input/output), or machine learning algorithms
- Comfort with working independently and in a team
Preferred Knowledge, Skills, and Abilities:
- Prior internship/work experience in Machine Learning or Software Development
- Familiarity with Pytorch or other deep learning libraries
- Prior experience/coursework in experimental physical sciences or engineering
- Experience with git and/or sphinx documentation
Environmental, Health & Safety Requirements:
- Ability to sit for extended periods to utilize computers for data analysis and programming.
Brookhaven National Laboratory and the Energy and Photon Sciences Directorate are committed to your success. We offer a supportive work environment and the resources necessary for you to succeed.
BNL takes affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities.We ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.Please contact us to request accommodation.
Brookhaven Science Associates requires proof of a COVID-19 vaccination for all employees.Proof of full vaccination as recognized by the CDC and/or WHO, inclusive of the two-week waiting period, is required either by November 17, 2021 or at the start of your employment if after November 17, 2021.
*VEVRAA Federal Contractor
Brookhaven employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation at the time of hire for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/fileApply Now