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mechanical design and analysis. Ability to work with a diverse group of researchers, engineers, and technicians. ANSYS, COMSOL, Python, Solidworks, CREO. Physical Requirements: Office environment. Working
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RF electronics. Proficiency with Python, CAD programs (e.g., SolidWorks), and COMSOL and knowledge of theoretical cosmology or particle physics is desired. Appointments are for one year with
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for renewal depending on departmental need. An advanced degree is required; PhDs or doctoral candidates preferred. Please provide a cover letter, curriculum vitae, a brief statement of your teaching experience
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psychology. Initial appointment is for one year with the possibility of renewal based on satisfactory performance and continued funding. Essential Qualifications: PhD in a relevant field. Interested
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to conduct numerical studies for non-local flow-driven magnetized plasma instabilities and their associated nonlinear transport in astrophysical and laboratory plasmas. The applicant is required to have a PhD
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along with experience in applying Artificial Intelligence (AI)/Machine Learning tools, OR a PhD in AI/Machine Learning with experience in plasma physics or other strongly coupled physical systems
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degree. Eligible candidate must have less than three years of post- PhD research experience prior to anticipated start date. Academic excellence, potential to bring new ideas and approaches to Princeton
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. Individuals must possess a PhD in a related field of interest to the department and have experience teaching at the undergraduate and/or graduate level. The successful candidate will lecture according
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and/or Spring 2025 semesters, depending on departmental need. Candidates should apply online at https://puwebp.princeton.edu/AcadHire/position/34561. PhD required. Please submit a cover letter which
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proposed stellarators (50%). Education and Experience PhD in Plasma Physics or a closely related discipline, or the expectation of completing the PhD before joining PPPL. Knowledge, Skills and Abilities