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of these metabolites. In addition, the candidate may explore the biological functions of these metabolites using preclinical models and clinical cohort data. The successful candidate also will mentor and supervise
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thus that allowing to scale up to thousands of species per year; To build Genera , a multimodal AI system that integrates genomic and ecological data to assess extinction risk and generate actionable
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computational models in pandemic science. These models can be applied in any relevant field! Sociology, Computer Science, Psychology, Game Theory, Immunology, Physics, and/or Ecology. Yes! Any/all of them! We
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on computers, standing, lifting 10 lbs. A well-qualified candidate for this position will also possess: PhD in Biology, Microbiology, Molecular Biology, Biochemistry. Research experience in model eukaryotic
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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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Position Description Postdoctoral Associate, Psychology College of Human Ecology, Cornell University This postdoctoral position is funded by an R01 grant (1R01AG082954-01) from the National
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: Immediately available Group or Departmental Website: https://www.simonsfoundation.org/neuroscience/simons-collaboration-on-ecological...(link is external) How to Submit Application Materials: Email your
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forest ecosystems traced by its isotopes: a modeling approach. Chem. Geol. 560, 119994. https://doi.org/10.1016/j.chemgeo.2020.119994 . Christina, M., Nouvellon, Y., Laclau, J. P., Stape, J. L., Bouillet
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internationally and domestically. Required Qualifications: The Postdoctoral Research Fellow will have completed a Ph.D. with substantial emphasis on disease ecology, habitat suitability modeling, computer vision
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modeling. The lab houses a state of the art mass spectrometry facility that specializes in metabolomics measurements of marine microbes and microbial communities. The successful candidate will work within a