Sort by
Refine Your Search
-
satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Essential Qualifications: PhD in a relevant discipline. Interested applicants must apply online
-
, strive for excellence, and support continuous quality efforts. As a member of the UHS staff, your job may be deemed essential as defined by University and UHS policy. Please ask you supervisor
-
of epistemic values in scientific practice, or the expression of values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical
-
fellowships and grant supported positions, but you will be asked in the application which positions you are interested in. For further inquiries, contact astropd@princeton.edu. PhD is required. The work
-
meetings. A PhD is required in a related field (e.g., demography, sociology, economics, epidemiology). Salary and full employee benefits are offered in accordance with University guidelines. Applicants
-
their residency at Princeton to assisting with research and to their own work. Eligible candidate must have less than five years of post-PhD research experience prior to anticipated start date. This is a one-year
-
laboratory plasmas. The applicant is required to have a PhD in physics, or a related field, and have a strong background in computational astrophysics. The expected starting date is negotiable. Appointments
-
, experience writing proposals for user-facilities, and/or experience leading and mentoring graduate and undergraduate students. A PhD in relevant fields of energy storage, electrochemistry, and materials
-
: PhD in a relevant discipline. Interested applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/37762 and include a cover letter, curriculum vitae, a brief statement of
-
Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation