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learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an
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mission is research and education in artificial intelligence, specifically focusing on natural language processing, computational linguistics, and data science. The main lab research areas are Arabic
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. In addition to these research tasks, you will also be assigned occasional teaching duties in one of the participating faculties. Where to apply Website https://www.academictransfer.com/en/jobs/363418
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making, cognitive science, and quantitative modeling. This postdoctoral appointment is full-time and on-campus. Job Duties 80% – Research Develop computational models of dynamic judgment and decision
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 20 days ago
Proposed Start Date 01/04/2027 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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advantage. The appointment is a training or transitional period in preparation for an academic, industrial, governmental, or other full-time research or teaching career. Additional emphasis will be placed
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within the group. As postdoc, you will principally carry out research. A certain amount of teaching may be part of your duties, up to a maximum of 20% of working hours. Qualifications To be qualified
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Responsibilities The successful candidate will: Synthesize therapeutic peptide vaccine candidates using Fmoc solid-phase peptide synthesis (SPPS). Develop and optimize peptide conjugation strategies, including CuAAC
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, NY. Postdoc position responsibilities will include: • Conduct epidemiological and statistical analyses of large complex datasets • Develop final publishable-quality tables and figures for inclusion in
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to develop algorithms to factor linear differential operators using p‑adic techniques. The central question of this proposal concerns the efficient symbolic resolution of linear differential equations