Job ID 2362 Date posted 10/28/2020
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.
The CFN is a DOE-funded national scientific user facility, offering users a supported research experience with top-caliber scientists and access to state-of-the-art instrumentation. The CFN mission is advancing nanoscience through frontier fundamental research and technique development, and is the nexus of a broad collaboration network. Each year, CFN staff members support the research of nearly 600 external facility users.
Three strategic nanoscience themes underlie the CFN scientific facilities: The CFN fosters research on complex self-assembly processes, for building new ways of constructing Synthesis by Nanomaterial Assembly. The CFN researches and applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery for target structure and functionality. The CFN develops and utilizes advanced capabilities for studies of Nanomaterials in Operando Conditions for characterizing materials and reactions at the atomic scale in real-world environments.
The CFN is seeking two exceptional Postdoctoral Research Associates to contribute to a collaborative, multidisciplinary project involving several DOE user facilities, focused on accelerating materials discovery.The project will apply Artificial Intelligence and Machine Learning (AI/ML) techniques to analyze complex multi-modal experimental and simulated data sets. In this project, you will carry out research to interpret X-ray spectral data using theory, computation and machine learning methods. You will have an opportunity to engage in developing computational spectroscopy databases and machine learning models to unravel correlations among local structure motifs, electronic descriptors and spectral features. In this research, you will work on data analytics driven projects directly tied to experimental data under the supervision of Dr. Deyu Lu, in close collaboration with researchers at CFN, the National Synchrotron Light Source II, and the Brookhaven Computational Science Initiative.
Required Knowledge, Skills, and Abilities:
You are qualified for this Research Associate position if:
- You have earned a Ph.D. in a relevant discipline (Physics, Chemistry, Materials Science, or a related engineering discipline) within the past five years or will complete your degree prior to the starting date;
- You have demonstrated track record in applying first-principles electronic structure theory to materials science and/or developing machine learning models and applications to solve science problems;
- You communicate effectively, both verbally and through technical writing, as demonstrated by peer-reviewed journal publications or conference presentations;
- You are committed to fostering an environment of safe scientific work practices.
Preferred background and experience:
You are well-matched to this position if:
- You have demonstrated knowledge of first principles computational spectroscopy;
- You have demonstrated knowledge of deep learning methods;
- You work effectively in a collaborative team to tackle challenging scientific problems, particularly the application of theory, simulation, and/or data analytics to understand experimental results;
- You are interested in learning how to use physical theory and computation together with deep learning methods.
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.
At Brookhaven National Laboratory, we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Our benefits program includes, but is not limited to:
- Medical, Dental, and Vision Care Plans
- Flexible Spending Accounts
- Paid Time-off and Leave Programs (vacation, holidays, sick leave, paid parental leave)
- 401(k) Plan
- Flexible Work Arrangements
- Tuition Assistance, Training and Professional Development Programs
- Employee Fitness/Wellness & Recreation: Gym/Basketball Courts, Weight Room, Fitness Classes, Indoor Pool, Tennis Courts, Sports Clubs/Activities (Basketball, Ping Pong, Softball, Tennis)
Brookhaven National Laboratory (BNL) is an equal opportunity employer that values inclusion and diversity at our Lab.We are committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a veteran, disability or any other federal, state or local protected class.
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.
*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 486.1. 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/@@images/file
To apply for this job please visit jobs.bnl.gov.