We invite you to apply for a Postdoctoral Appointee position with our Chemical and Fuel Cycle Technologies Division (CFC).
The successful candidate for this position will perform research in machine/deep learning to advance physics-aware artificial intelligence technology and applications. The successful candidate will work with a team of structural/mechanical engineering researchers to develop physics-guided machine/deep learning based algorithms, including designing machine/deep learning architecture incorporating physics models for broad artificial intelligence applications in computer vision, computational imaging, infrastructure sensing and identification, etc.
Position Requirements: We expect you to have:
- Knowledge and background (Ph.D.) in Applied Mathematics, Computer Science, Statistics, or related fields.
- Experience in machine/deep learning, signal/image processing, computer vision, optimization through scholarly publications or equivalent.
- Programming experience in C, C++, and/or Python.Experience in TensorFlow, Keras, and/or other platforms.
- Strong communication skills both verbal and written.
- Software experiences and techniques for computational science problems and practical applications are preferred, but not required.
To be eligible, candidates must have completed their Ph.D. within the last three years.
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 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, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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