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, especially those planning to pursue a future PhD, DVM, or MD. Learning Objectives: Under the guidance of a mentor and in close collaboration with senior laboratory personnel, you will have the opportunity
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telemetry to support holistic situational awareness and decision support in combat casualty care Adapting large vision-language models (VLMs) and multi-modal foundation models, including prompt engineering
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, Computer Science, Systems Engineering, or a closely related field. A postgraduate is required to have earned their degree within 5 years of the appointment start date. A PhD is not required at the time of selection
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, plant water/nutrient hydraulics, chlorophyll fluorescence, and root system architecture adaptation. Controlled (greenhouse/glass house/growth chambers) and field research experience. Comprehensive
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of Salmonella survival, adaptation, persistence, and transmission in these systems, you will engage in the development of science-based strategies aimed at reducing Salmonella contamination during pre-harvest
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matter experts coordinate during a public health emergency. Build skills in managing priorities and adapting to changes in a fast-paced emergency response environment. Mentor(s): The mentor
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to classify rangeland plant species, and (2) using transfer learning to adapt deep learning models for imagery analysis to varying UAV sensors and conditions. These techniques will allow you to identify and
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examining factors associated with diabetes prevention, management, self-care, awareness, and health outcomes. Strengthen skills in longitudinal analyses of diabetes incidence, prevalence, complications
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expand your research skills and knowledge under the mentorship of PhD-level scientists in entomological-based research, experimental design, and data analysis. Mentor(s): The mentor for this opportunity is
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application of foundation models for fungal DNA and protein sequences. With ARS and external AI knowledge-holders, you will adapt long-context DNA language models to fungal genomes. These DNA-language models