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of squamate reptiles; the largest group of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus
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, with appropriate research experience in quantitative biology, (bio)physics, (bio)engineering or related Engineering and Physical sciences disciplines, and a solid publication record. We seek faculty
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://puwebp.princeton.edu/AcadHire/position/35001 Review of applications will begin immediately and remain open until the position is filled. Applicants should submit the following materials: cover letter, curriculum vitae
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, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other
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/ Affirmative Action Employer, and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national
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configured as a broad beam ion source, atom source, or atom/ion hybrid source. Synchrotron studies may also be conducted. Qualified candidates should have a Ph.D. in chemistry, physics, materials science and
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computer programming in R and Python *Formal training or experience applying quantitative and spatial methods to human-environment questions *Excellent academic writing and communication skills in English
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materials must be submitted by the application deadline date of May 31, 2025, 11:59 p.m. EST. These positions are subject to the University's background check policy. This position is an in-person on campus
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for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-CBE-00001
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/or energy *Strong methodological and quantitative skills, such as survey and sampling design and data analysis (in R or Python), meta-analysis and/or document/text analysis, or computational modeling