[Neutron] Job Posting : Postdoctoral Research Associate in Total Scattering and Machine Learning

[Olds, Daniel]

Brookhaven National Laboratory - USA
The speed of modern synchrotron data collection has outpaced typical data analysis methods for years.  This is particularly true for operando measurements, for example, in the field of heterogenous catalysis where structure-property relationships under reactive/in use conditions are critical for new technological advancements.
The goal of this project is to develop a suite of tools to aid in the measurement and prompt analysis of data from NSLS-II via machine learning methods.  These tools will be commissioned on in-situ powder diffraction measurements of catalytic nanomaterials under their reactive, deteriorative, and regenerative environmental conditions. The resulting suite of tools will be deployed broadly around the facility, greatly impacting the way current and future users of synchrotron light sources approach their science.
This project spans subject matter areas from material science to data analysis and scientific computing.  The successful candidate will work closely with both the X-ray Powder Diffraction beamline teams (XPD/PDF) and the Scientific Computing & Data Acquisition, Management, Analysis Group (DAMA) at NSLS-II to develop the methods.
This position is an excellent opportunity to develop expertise in both X-ray scattering and scientific computing, and the successful candidate is not required to already be experienced in both, only willing to learn.

Essential Duties and Responsibilities:

  *   Spearhead a focused effort to develop machine learning and AI methods for total scattering studies of energy materials.
  *   Develop machine learning and AI methods that will be integrated with the Bluesky data acquisition framework at NSLS-II.
  *   Collaborate with a wide range of researchers across a variety of disciplines including energy storage, catalysis, geology, complex oxides, quantum materials, and machine learning and AI methods both within BNL and externally.
  *   Publish and presents the results of these studies to the community.

Required Knowledge, Skills, and Abilities:

  *   PhD degree in the Physical Sciences, Material Science, Applied Mathematics, Computer Science or related field.
  *    Experience with neutron or X-ray powder diffraction, XAFS, imaging, or a related technique.
  *    Demonstrated record of scientific excellence through publications and talks.
  *   Ability to work collaboratively with a diverse, discipline spanning team of scientists and engineers.

Preferred Knowledge, Skills, and Abilities:
Experience in one or more of the following:

  *   Experience creating data analysis methods and procedures.
  *   Demonstrated record in collaborative software development, especially in distributed teams.
  *   Experience in data acquisition and analysis at a synchrotron light source, neutron source or other major scientific user facility.
  *   Experience with Python.
  *   Experience with material science, solid-state chemistry, or catalysis.
  *   Experience with total scattering and PDF methods.

For more information and to apply, please see the full job posting:
https://jobs.bnl.gov/job/upton/postdoctoral-research-associate-in-total-scattering-and-machine-learning/3437/14611841
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