68 algorithm-development-"Prof"-"Washington-University-in-St" Postdoctoral positions at Princeton University
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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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developing biotechnological solutions to address challenges in renewable energy, sustainable manufacturing, and human health. Our group specializes in dynamical and spatial control of engineered metabolisms
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Tufekci in the field of social movements, especially as how it relates to changes in the public sphere with technological developments. The position will be awarded to an emerging scholar who will devote
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assisting with managing the lab and projects. We also expect that you will collaborate with the ARG team on developing grant proposals. Qualifications Required qualifications: Doctoral degree in a related
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collaborate with the ARG team on developing grant proposals. Qualifications Required qualifications: Doctoral degree in a related field, such as Architecture, Civil Engineering, Robotics, etc. Excellent track
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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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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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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
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courses. Program faculty and senior researchers will offer mentoring to support professional development. Former postdoctoral researchers with SGS have pursued careers in academia, nongovernmental and
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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