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://phoenixmed.arizona.edu/cts/phd Location Greater Phoenix Area Address Phoenix, AZ USA Position Highlights The Administrative Support Assistant II will support the coordination and execution of departmental projects with a
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and online Master of Science in Finance (MSF/OMSF), and PhD programs to successfully promote, coordinate, and implement these graduate programs. The successful candidate will be self-motivated; have a
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murine BMT models, and donor graft manipulation and pharmacological interventions. Candidates should have a PhD or MD and a strong foundation in cellular and/or tumor immunology. Candidates with a
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. Preferred Qualifications PhD in Political Science, Public Policy, or related subject relevant to class assignment. Rank Instructor Tenure Information Adjunct (NTE) FLSA Exempt Full Time/Part Time Part Time
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locations for data collection Will need to help organize data collection locally, domestically, and international Minimum Qualifications PhD in relevant field Preferred Qualifications FLSA Exempt Full Time
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://phoenixmed.arizona.edu/cts/phd Location Greater Phoenix Area Address Phoenix, AZ USA Position Highlights The Radovick Lab at UArizona Phoenix has an opening for a post-doctoral researcher to study the role
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assigned as needed. Minimum Qualifications PhD, or DC Preferred Qualifications 1 year of prior experience teaching these labs and performing these responsibilities in the CA block at the COM-Phoenix. Rank
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Hire Date 8/18/2025 Expected End Date Contact Information for Candidates Carol Barnes, PhD carol@nsma.arizona.edu Open Date 7/15/2025 Open Until Filled Yes Documents Needed to Apply Curriculum Vitae (CV
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starting August 1, 2025. The Department of East Asian Studies offers Bachelor of Arts degrees and offers programs leading to Master of Arts (MA) and Doctor of Philosophy (PhD) degrees in East Asian Studies
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Qualifications PhD in data science, biomedical informatics, computational biology, neuroscience, or related field. Demonstrated experience in analyzing real-world medical data and applying machine learning