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Qualifications PhD degree in computer science, data science, or related fields. Effective written and verbal communication skills Robust machine learning and natural language processing skills Proficient computer
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human vagus nerve stimulation and functional effects across the body. Experience in processing and modeling imaging data with predictive models. Experience in grant writing and working with large grant
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professional domains and a desire to ask big-picture questions. Preferred Qualifications: Familiarity with natural language processing (NLP) or network science. Experience with machine learning libraries like
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, electrophysiology, imaging, genetic and whole-animal cardiovascular assays. This position represents an opportunity to work side-by-side with talented graduate students, postdocs, and specialists in a dynamic and
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that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post- doc and/or in an R&D position, excluding
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ways to reduce agricultural or food waste and introducing new food or material products in waste management systems. The ideal candidate will bring expertise in fermentation and non-chemical processing
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unique angle to discover new biological/cellular processes in development and disease. Combining sophisticated genome engineering and innovative live cell imaging, we have pioneered in vivo studies
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on the scientific potential/achievement of the candidates, technical experience/expertise in developing an imaging data-processing pipeline would be highly desirable. We are especially interested in candidates
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, NEON airborne remote sensing, comprehensive water balance measurements, soil respiration data, and tower-based remote sensing including multiple thermal imagers and solar-induced fluorescence (SIF
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engineering-based research applicable to cardiovascular disease as part of an extramurally funded project. The successful applicant will create computational models from medical imaging data and run simulations