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questions, please email Dr. Mostafa Mobasher at mostafa.mobasher@nyu.edu . Applications may be submitted on https://nyuad.nyu.edu/en/about/careers/postdoctoral-and-research.html . About NYUAD: NYU Abu Dhabi
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- Support mentoring of graduate students where appropriate Qualifications - PhD in Physics, Materials Science, Chemistry, or a closely related field - Strong background in electronic structure theory
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, electronic properties, and spectroscopic response. A major component of the project will focus on coupling DFT calculations with machine learning models to accelerate spectral prediction, identify robust
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physics (mandatory) Good didactic skills (mandatory) High written and oral expression skills (mandatory) Computer user skills (mandatory) Excellent command of English (mandatory) Ability to work in a team
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, and a proven ability to communicate scientific information across diverse audiences. A minimum of one year of post-PhD research experience, a track record of lead-author publications, experience in
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telescope network). Ability to connect with other researchers in the department as well as campus-wide will also be considered positively. The applicant must have completed a PhD in Astronomy, Physics, or a
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internal governance processes, including work package coordination, decision tracking, and follow-up actions across partners. Applicants must have: A PhD degree in a relevant field (e.g., quantum technology
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We expect you to have a PhD (or equivalent) degree in mathematics, a strong research background in analysis with emphasis on areas supporting one or several of the research topics above, and motivation
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contributing to knowledge exchange. Supporting the supervision of junior researchers where appropriate. Person Specification Essential: PhD (or near completion) in mathematics, statistics, computer science
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this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert knowledge of deep