Physically Informed Machine Learning Researcher
Physically Informed Machine Learning Researcher
Project Description:
Process Modeling using Physically Informed Machine Learning
Key Responsibilities:
- Design and train physics-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements
- Implement algorithms to assess the performance of PIML models
- Assess uncertainty in the predictions of PIML models
- Develop systems for multiscale modeling of atomic layer deposition processes
- Develop software to implement the goals stated above (most likely in Python)
- Disseminate results through posters/seminars and international meetings and meeting seminars
- Ensure that all results, findings, data, software, etc. are correctly archived and transmitted through appropriate channels
- Algorithm development, implementation, and analysis
- Analyze heterogeneous data sources
- Present results at internal meetings, and occasional meetings with external stakeholders
- Ensuring that results, protocols, software, and documentation have been archived or otherwise transmitted to the larger organization
Desired Qualifications:
- U.S. Citizen Preferred
- Ph.D. in Chemistry, Physics, Mathematics, Computer Science, Data Science, or a related field
- Significant course work in one or more of chemistry, physics, mathematics, statistics and/or computer science
- Familiarity with one or more chemical process modeling packages (e.g. Cantera, CHEMKIN)
- Familiarity with one or more AI/ML software packages (e.g. Tensorflow or Pytorch)
- Ability to program in a modern computational language (e.g. Python)
- Strong oral and written communication skills
Other Details:
- Full-time: the participant is expected to work 40 hours a week
- Location: the participant will work at the NIST Gaithersburg Campus.
- Duration: this is expected to be a one-year position. Extensions are sometimes granted depending on the availability of funds.