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availability of funding. The anticipated start date for the position is June 1, 2025. Individuals with a strong theoretical background who expect to obtain a PhD in a related field (e.g., statistics
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leverage these findings for bioengineering applications. Candidates completing (i.e., with a confirmed defense/viva date) or holding a PhD in chemical engineering, physics, bioengineering, chemistry, or a
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, machine-learning for biological data, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve goals in the next stage of
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. These models will be used to design and test policy and investment interventions to alleviate deployment bottlenecks. The successful candidate will have experience with applied energy systems analysis, economy
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subdivisions of the squamate body plan. The candidate will work towards developing computational resources that assist in the data management and analysis of genomic data and its integration with phenotypic data
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at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. A PhD is required
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excellence in education are encouraged to apply. PhD is required. Applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/37861 and include curriculum vitae, research statement and names
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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nano-plasmonics) design, fabrication, and characterizations. All candidates should have a Ph.D. degree. Appointments will be for one year, with the possibility of renewal pending satisfactory performance