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Machine Learning for Materials Synthesis and Processing - Senior R&D Staff

Date: Jun 10, 2021

Location: Oak Ridge, TN, US, 37831

Company: Oak Ridge National Laboratory

Requisition Id 4340 

Overview: 

We are seeking a senior research staff member who will focus on the applications of the machine learning and artificial intelligence methods for design an doptimization of materials synthesis workflows. This position resides in the Data NanoAnalytics group in the Theory and Computation Section, Center for Nanophase Materials Sciences (CNMS), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL).  

 

As part of our research team, you will lead an effort on the development of the machine learning (ML) and artificial intelligence (AI) methods for guiding the synthesis of the broad range of functional materials ranging from wet chemical synthesis, pulsed laser deposition/combinatorial systems, and continuous microfluidic and polymer synthesis. The experimental systems enabling this research are being developed across the multiple research groups in the physical sciences directorate. The candidate is expected to collaborate with these research groups, the theory effort in the PSD, and broad computational effort in the Computing and Computational Sciences directorate at ORNL to develop the physics-based optimization workflows to explore the broad compositional and processing spaces available in these systems. Of special interest is the utilization of the rapid and in-situ characterization results as low-latency proxies for the active state of the system, and incorporation of the first-principles calculations and prior knowledge in the form of literature data, structural libraries, or known physical laws into the workflows beyond the simple Bayesian optimization.

 

Major Duties/Responsibilities: 

  • Development and deployment of the physics-based ML/AE workflows for exploration of broad composition and synthesis spaces on automated experiment platforms
  • Synergistic interaction with the characterization experts in CNMS for incorporation of post-experiment imaging data and in-situ proxy characterization data into the analysis
  • Collboration with the scientists in PSD and CCSD on incorporation of the physical priors in the form of DFT and other simulations and prior knowledge into the analysis

 

Basic Qualifications:

  • A Ph.D in materials science or condensed matter physics with the focus on machine learning methods with a minimum of 6 years of experience
  • Familiarity with classical experiment planning methods based on Gaussian Processing, Bayesian Optimization, and/or Reinforcement Learning
  • Domain knowledge in at least one area of the chemical synthesis (wet chemistry, pulsed laser deposition, microfluidic systems, etc)
  • Advanced level of Python programming

 

Preferred Qualifications:

  • Excellent written and oral communication skills
  • Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory 
  • Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

 

Special Requirements

Three letters of reference are required and can be uploaded to your profile or emailed directly to PSDrecruit@ornl.gov.  Please include the title of the position in the subject line.

 

This position requires access to technology that is subject to export control requirements. Successful candidates must be qualified for such access without an export control license.

 

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.


If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.


ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.


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