Location: Albany,NY, USA
Location: Albany, NY Category: Research Foundation Careers Posted On: Wed Sep 6 2023 Job Description:
Description of Duties:
Job responsibilities include, but are not limited to:
The post-doctoral fellow will program machine learning software to analyze data from materials characterization methods such as transmission electron microscopy, electron diffraction from transmission electron microscopy or electron backscattering, X-Ray diffraction, X-ray photoelectron spectroscopy, and optical methods such as ellipsometry and scatterometry. The post-doctoral fellow should be familiar with programing and use of machine learning software and Python. Experience in materials characterization including microscopy is highly desired. The initial project will involve analysis of electron diffraction maps from ferroelectric thin films for phase and crystal orientation. The data from these methods will be obtained and simulated for samples provided by TEL Technology Center USA. The post-doctoral fellow will work closely the scientists and engineers at TEL Technology Center USA. Teaming skills are essential. A link to Professor Diebold's research group is: Diebold's Research Group | University at Albany. (
Other reasonable duties as assigned.
Applicants must address in their applications their abilities to work with a culturally diverse population. This position is contingent on the satisfactory completion of a background check; this position may require annual background checks.
Job Requirements:
Minimum Qualifications:
+ The candidate must have obtained a PhD in Physics, Materials Science, Chemistry, or Computer Engineering within 3 years of their application and must have considerable experience programing machine learning software.
+ The post-doctoral fellow should be familiar with how to program software such as ResNet50 and Xception or the equivalent for machine learning applications.
+ The candidate must be able to write proficiently had have a record of publishing in peer reviewed journals on machine learning topics.
+ The degree should come from a college or university accredited by the US Department of Education or internationally recognized accrediting organization.
+ Candidates must address in their applications their ability to work with culturally diverse populations.
+ This position is contingent on the satisfactory completion of a background check; this position may require annual background checks.
Preferred Qualifications:
+ The candidate should have proven experience programing machine learning software to analyze data from materials characterization methods such as one or more of the following: transmission electron microscopy, electron diffraction from transmission electron microscopy or electron backscattering, X-Ray diffraction, X-ray photoelectron spectroscopy, and optical methods such as scatterometry.
Notes: Position contingent on the availability of funding
Additional Information:
Salary: $50,000-$60,000
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As an Equal Opportunity/Affirmative Action Employer, The Research Foundation for SUNY will not discriminate in its employment practices due to an applicant's race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), sexual orientation, gender identity or expression, transgender status, age, national origin, marital status, citizenship, physical and mental disability, criminal record, genetic information, predisposition or carrier status, status with respect to receiving public assistance, domestic violence victim status, a disabled, special, recently separated, active duty wartime, campaign badge, Armed Forces service medal veteran, or any other characteristics protected under applicable law.
The Research Foundation for The State University of New York offers exceptional benefits such as healthcare, dental, vision, pension plans, competitive pay, generous paid time off, tuition assistance, life insurance and long-term disability insurance. The University at Albany offers a variety of performing arts, world class libraries, and competitive sporting events.