Careers Job Description
General Description
This project focuses on data-driven research discovery in chemistry and involves designing and implementing machine learning methods for materials discovery, specifically in the field of solid state radiochemistry. The candidate is expected to collect reliable data on materials, develop descriptors, apply machine learning methods, and validate the predictions using experiments. The candidate should have proven track record in academic and industrial settings, with expertise in experimental characterization and synthesis techniques. Experience in solid state synthesis and material characterization is required (high-temperature solid-state reactions, powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy). Machine learning skills in Python and industry-standard libraries (TensorFlow, Scikit Learn, NumPy, and Pandas) along with scientific computing skills are necessary. In addition, the candidate is expected to manage the laboratory operations and maintain equipment and supplies. Previous postdoctoral, industrial, or startup experience is required with a proven record of first-author peer-reviewed publications, presentations, and workshop teaching/training. Hands-on training and mentoring of undergraduate, graduate, and postdoctoral researchers is required.
Other Duties
The successful candidate should be able to train and work with graduate students and undergraduates on this project. The candidate will be writing up manuscripts and giving presentations on their work. It is highly encouraged to develop and teach Material Informatics course and participate in regularly organized machine learning workshops. The candidate will be working with faculty and staff at Hunter College and collaborators at national laboratories.
Qualifications
- Extensive experience in machine learning assisted materials discovery, as demonstrated through published works.
- Experience in material synthesis using high-temperature methods and their characterization.
- Ability to develop material structure maps by developing interpretable descriptors.
- Experience in Python and relevant libraries, as demonstrated by GitHub projects or publications.
- Ability to train students and staff in machine learning, as it pertains to materials chemistry.
- Analyze, interpret, and report research results and assist in the preparation of manuscripts and grant applications.
- Excellent communication skills, that is written and oral communication.
- Experience in active learning methods is an asset.
RFCUNY Benefits
RFCUNY Employee Benefits and AccrualsAbout the Research Foundation
The Research Foundation of The City University of New York (RFCUNY) was established as a not-for-profit educational corporation chartered by the State of New York in 1963. RFCUNY supports CUNY faculty and staff in identifying and obtaining external support (pre-award) from government and private sponsors and is responsible for the administration of all such funded programs (post-award).
RFCUNY stands between CUNY’s principal investigators (PIs) and the sponsors who support them and strives to fulfill its essential responsibilities to both groups. Working closely with individual PIs and Grants Officers on the campuses, RFCUNY oversees employment, accounting, audit, reporting, purchasing, and special responsibilities that include management of a planned giving program; liaison with governmental agencies and foundations; negotiation of agreements; facility construction and renovation; protection and commercialization of intellectual property; and compliance with applicable standards in research involving human subjects, animal care, environmental and radiological safety, and conflicts of interest.
Equal Employment Opportunity Statement
Key Features
Chemistry
Full Time
$70,000.00 - $70,000.00
Nov 11, 2024 (Or Until Filled)