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Field
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Implementation, further development and adaptation of AI models and research prototypes for the annotation and scoring of text data, in particular for the optimization of AI-supported teaching and learning in
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genetics in the grasses, especially in the model systems Zea mays (maize) or Brachypodium distachyon. We particularly welcome candidates with expertise in grass transformation and/or spatial transcriptomics
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that underlie typical and atypical developmental outcomes for children and young people, with a particular focus on developing evidence-based practice models of benefit to families, schools, teachers, community
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adulthood. We use a wide variety of behavioral, modeling, and neuroimaging techniques. Please see https://agingwell.utdallas.edu/ for more information on our lab’s focus and ongoing projects. Minimum
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-specific needs, but include: site evaluations or modeling, project coordination, interviews, data collection and/or analysis. Connections to professional practice and to emerging technology and ideas in
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qualification is required at time of hire. Required Qualifications Completed Ph.D. in Ecosystem or Landscape Ecology (or related field such as ecosystem modeling, biogeochemistry, or climate change ecology
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perfusion as people age with various comorbidities (hypertension, hypotension, obesity) will preserve oxygen supply to the brain. MRI and two photon imaging are used in animal models to understand
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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
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and robustness of defenses and mitigations for AI systems, reverse engineering AI systems and models, and identifying new areas where security research is needed. We participate in communities
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collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with