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communities using model and non-model organisms. The individual is required to use tools in chemical ecology, genomics, molecular biology, and AI-driven predictive modeling in the research project. Specific
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computational research. They are intrinsically driven, goal-oriented, and can work collaboratively with others. Working closely with the CPS divison, the postdoc will leverage AMReX and the LBM to develop
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pioneer of purpose-driven science and Cornell University’s second largest college. We work across disciplines to tackle the challenges of our time through world-renowned research, education, and outreach
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Postdoctoral Associate - Sustainable Energy and Synthetic Biology The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest
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for professional development, various types of leave, and options for sports and cultural activities. You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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are seeking candidates with a strong track record in cancer research, with exceptional verbal and written communication skills, who are eager to work on innovative, hypothesis-driven projects and contribute
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interfaces, and condensation surfaces under varied operating scenarios. Develop and refine performance simulation models and predictive tools to support system optimization and deployment strategies. Prepare
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materials recovery,CO2 electrolysis and fuel cells. Experimental work will involve design, characterization, and degradation studies of model interfaces that can help elucidate their degradation mechanisms
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quantitative ecology, applied statistics, or a related field with strong background in statistics and model development. Experience with R and analyzing spatial datasets. Ability to apply quantitative methods