Graduate Student

Project
PREP0004370

We propose to develop physics- and empirically informed digital twin representations for high-
impact SI realizations of mass, voltage, and resistance that are advancing towards
commercialization. Leveraging recent advancements in artificial intelligence (AI), including
neural networks and digital twins, we expect to bypass the conventional, iterative prototyping
and development cycle. Rather than taking decades to achieve an optimal design, our digital twin
representations will enable rapid, offline optimization of their respective real-life embodiments,
significantly accelerating time to market.

SI meets AI: Neural Network-powered Digital Twin for Advancing Primary Standards

Qualifications
  • US citizenship is preferred.
  • PhD Candidate in Physics with 2 or more years of relevant experience.
  • Expertise in Pytorch/Python and state of the Art AI models like vision transformers and advanced
    CNNs.
  • Ability to build deployable complex software solutions for image reconstruction.
  • Strong oral and written communication skills and strong presentation skills.
Research Proposal

Key responsibilities will include but are not limited to:

  • Exploring AI networks for Regression models of Physical SI measurements
  • Exploring AI networks for image classification of graphene data
  • Create presentation material of the results
NIST Sponsor
Joe Chalfoun
Group
Applied AI Research Group
Salary / Hourly Rate {Min}
$35,000.00
Schedule of Appointment
Full time
Start Date
Sponsor email
joe.chalfoun@nist.gov
Work Location
UMD Campus
Salary / Hourly rate {Max}
$55,000.00
Total Hours per week
20
End Date
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