Postdoctoral Appointee – Mechanical Engineering
- Argonne National Laboratory
- Location: Argonne, IL
- Job Number: 7101108 (Ref #: 413483)
- Posting Date: May 28, 2022
- Application Deadline: Open Until Filled
The Applied Materials Division at Argonne National Laboratory has an immediate opening for a postdoctoral appointee. The candidate will apply statistical inference and machine learning techniques to experimental data and physics-based simulation results to develop predictive, statistical models for the long-term behavior of materials under challenging service conditions.
The candidate will work closely with ANL project PIs on improving numerical methods currently used by the project team to develop and fit material models. The project work will also involve developing and evaluating new methods, implementing these methods using Python, and evaluating the methods against experimental and simulation data. The candidate will help draft and publish technical reports, conference paper, and journal publications describing the research and will be expected to attend and present at technical conferences.
Safety, Security, and Environmental Protection: All activities, as they apply to work performed by self or by personnel under supervision, will be executed in compliance with ES&H and security responsibilities established by Argonne National Laboratory's ES&H policies, Safeguards and Security policies, work rules, and safe practices.
This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned.
The prospective candidate is expected to hold a Ph.D. in mechanical engineering, materials science, civil engineering, structural engineering, or a closely related field.
Knowledge and experience in optimization methods, especially for calibrating models against data, is highly desired.
Extensive knowledge of non-parametric regression machine learning techniques.
Experience using Bayesian inference, ideally variational Bayes methods.
Experience using Pytorch and/or Pyro to implement and train models.
Strong skills in oral and written communications and presentations at all levels of the organization.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Specific experience developing statistical models for material or structural behavior.
The ability to work independently to accomplish project goals.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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