Postdoctoral Appointee – Mechanical Engineering
- Argonne National Laboratory
- Location: Lemont, IL
- Job Number: 7101108 (Ref #: 413483)
- Posting Date: May 28, 2022
- Application Deadline: Open Until Filled
Job Description
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.
Position Requirements
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.
Preferred Qualifications:
Specific experience developing statistical models for material or structural behavior.
The ability to work independently to accomplish project goals.
Job Family
Postdoctoral FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeAs an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
