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Science, Statistics, Applied Mathematics, or a related field. • Strong background in convex analysis, statistical machine learning (reinforcement learning, LLM and generative modeling), stochastic modeling and
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energetic costs in experimentally evolved populations. The ideal candidate should have prior experience and/or a strong interest in conducting genome and transcriptome sequencing and analysis, ribosome
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peer-reviewed publications Preferred Qualification Familiar with various chemical analyses and characterization instruments Experience in process modeling using Aspen or other software and TEA analysis
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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. The researcher will use statistical methods such as multilevel models and multivariate analysis of variance to take into account key variables and covariates to study the impact on students’ outcomes
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tools in heat and mass transfer, microfluidics, thermodynamics, bioengineering, micro/nanotechnology, and computational modeling. We are passionate about developing novel translational platform
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independently and as part of a team. Strong organizational skills and attention to detail. Experience teaching student and/or professional cohorts. Working knowledge of natural language processing, topic modeling