The Theory and Computation Group at the Center for Functional Nanomaterials (CFN) has three full time Postdoctoral Research Associate positions available at the Brookhaven National Laboratory (BNL). These positions are part of a collaborative, multidisciplinary project involving several U.S. DOE user facilities, focused on accelerating materials discovery. The proposed research is directed to applying artificial intelligence and machine learning (AI/ML) techniques to analyze complex multi-modal experimental and simulated data sets, with the primary focus on core-level spectroscopy data.

At these positions, you will carry out research in one of the two following areas. 1) X-ray spectral data interpretation 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. You will work on data analytics driven projects directly tied to experimental data under the supervision of Dr. Deyu Lu. 2) Development of data processing and interpretation pipelines for handling the streamed data from a suite of in situ / operando experimental facilities using modelling, machine learning, and software engineering tools. As a first implementation, you will apply these methods for processing and interpreting data from X-ray spectroscopy measurements. You will use the data pipelines you develop to condition the data, extract valuable scientific information and provide real-time feedback to the experiment.  In this research, you will work on data analytics closely coupled with in situ / operando experiments under the supervision of Dr. Xiaohui Qu. Both research directions are in close collaboration with researchers at CFN, the Computational Science Initiative and the National Synchrotron Light Source II at BNL. Position can start as early as January 2021. 

Qualifications of the candidates include a Ph.D. in physics, chemistry, materials science or a related engineering discipline within the past five years or will complete your degree prior to the starting date.

Preferred Skills:

• Demonstrated track record in applying first-principles electronic structure theory to materials science;
• A strong background in machine learning model training with applications to domain science problems;
• Software development and/or data analytics experience demonstrated through publications, GitHub repository records, or other public communications;
• Good communication skills, both verbally and through technical writing, as demonstrated by peer-reviewed journal publications or conference presentations.

To be considered for these positions, please apply online at http://jobs.bnl.gov/ (entering the Job ID number 2362 (2 positions) or 2367 (1 position) into the Search by Keyword).

Organization Overview:

Brookhaven National Laboratory is a multipurpose research institution funded primarily by the U.S. Department of Energy’s (DOE) Office of Science. Located on the center of Long Island, New York, Brookhaven Lab brings premier facilities and expertise to the most exciting and meaningful questions in basic and applied science—from the birth of our universe to the sustainable energy technology of tomorrow. We operate state-of-the-art large-scale facilities for studies in physics, chemistry, biology, medicine, applied science, and a wide range of advanced technologies. Brookhaven Lab employs nearly 3,000 scientists, engineers, and support staff, and engages more than 4,000 visiting researchers from around the world each year. Our award-winning history, including seven Nobel Prizes, stretches back to 1947, and we continue to unravel mysteries from the nanoscale to the cosmic scale. Brookhaven Science Associates, a nonprofit applied science and technology organization, operates Brookhaven Lab for the U.S Department of Energy.

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.