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research positions in the following fields:1. Microfluidic and Lab-on-Chip development in a multidisciplinary lab. Candidates should demonstrate track-records in microfluidics, Lab-on-Chip, and micro
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scientists for research and development activities focused on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution
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; biodiversity; conservation; environmental science and policy; infectious disease and global health; and sustainable development in impoverished and resource-challenged regions of the world. The Term
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starting July 2025, and will remain open until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine
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to both cities. This opportunity is ideal for:1) Immunologists or Developmental biologists (with expertise in mouse genetics, tissue processing/cell isolation, and/or flow cytometry) looking to develop
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position for new projects to characterize synthesis processes and novel materials in several research thrusts: i) development of advanced manufacturing processes for low-cost battery cathode active materials
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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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until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus
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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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist to develop hybrid models for sea ice that combine coupled climate models and machine