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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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projects and have a desire to learn, troubleshoot, and design new experimental or analytical approaches in a collaborative and supportive setting. Applications from members of groups historically under
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quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation
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to develop computational and analytical tools for optimizing magnetic field and coil shaping to achieve particle confinement, maximizing integrability and quasisymmetry. Applicants should have a PhD in
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to computational analysis of and machine-learning approaches to mass spectrometry-based metabolomics and/or proteomics data. Positions are available starting March 2024, and will remain open until excellent fits
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approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a range of exciting projects
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balance multiple demands with grace and good humor. Excellent analytical, problem-solving, and data-gathering skills. Preferred qualifications: Knowledge of Princeton University and Princeton's
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research rank for experimental work on plasma propulsion. The candidate must have PhD in engineering or physics with solid knowledge of plasma propulsion principles, and significant experience in working
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design new experimental or analytical approaches in a collaborative and supportive setting. Applications from members of groups historically under-represented in STEM are encouraged. The term
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shaping to achieve particle confinement, maximizing integrability and quasisymmetry. Applicants should have a PhD in physics or a related area. Candidates will be expected to be proficient with analytic