67 algorithm-development-"Multiple"-"Prof" "UNIS" Postdoctoral positions at Princeton University
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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senior researcher in the areas of soft materials and polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using
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The Department of Electrical and Computer Engineering has opening for postdoctoral research positions in the following fields: 1. Microfluidic and Lab-on-Chip development in a multidisciplinary lab
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-assembly, and soft condensed matter. The successful candidates will develop computer simulation approaches to understand compartmentalization inside cells (i.e., formation of biomolecular condensates) and to
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addition to the aforementioned project, the appointee will have opportunities to develop additional projects with members of Dr. Sinclair's lab and/or maintain their on-going work. The work location for this position is in-person
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Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks postdoctoral scientists or research scientists for research and development activities focused
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polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using advanced microscopy techniques and related methods
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days per week. Leveraging Princeton's scholarly resources, fellows will focus on research, expand their intellectual horizons, and prepare work for publication. They will have biweekly meetings with
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senior researcher in the areas of soft materials and polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using
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fields. Candidate must have excellent computational and bioinformatic skills; abilities for developing simulation models will be highly valued; experience with ancient DNA genomic datasets is encouraged