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Howard Hughes Medical Institute - Janelia Research Campus | Virginia Beach, Virginia | United States | 9 days ago
be part of a project at the intersection of protein engineering, evolution, and machine learning, which is part of HHMI’s AI for Science Initiative (ai.hhmi.org ). Our goal is to integrate
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silico approaches. This may include mathematical modeling of biological systems, machine learning and artificial intelligence methods, and the development of innovative algorithms and software pipelines
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statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets. What we offer: • A stimulating
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technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance
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collaboratively within an interdisciplinary research environment. Desirable experience with advanced AI or machine-learning methods beyond standard predictive modelling prior exposure to qualitative or mixed
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, nursing directions, scheduling templates, protocols, and staffing models. Skills / Knowledge / Abilities Basic computer knowledge, MS Windows, Word, Outlook, clinical applications. Supervisory experience
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in a university or college setting. Working knowledge of the content areas of probability, statistical methods, generalized linear models, statistical computing, and machine learning. Preferred
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or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated communities in
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. 3. Machine Learning and Predictive Analytics: • Develop and apply machine learning models (including Azure Machine Learning) to optimize healthcare data analysis accuracy. • Collaborate with data
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational