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electron microscopy (sbfSEM). These studies will also require advanced computational analysis of the data as well as the development of machine learning techniques to aid in said analysis. Key
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machine learning applications in ecology, open science ethics and data sovereignty for environmental data, data-intensive exploration of evolutionary processes, big data and environmental justice, or other
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sciences, data science, machine learning, statistics, mathematics, computer science, or related fields. Preferred Qualifications: Experience in at least one programming language such as Python or R
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life-long learning. Our faculty have a resolve to create an exciting academic environment that will build upon what is already recognized as one of the finest residency programs in the country, and by
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, the selected candidate will work on projects funded by the National Institutes of Health (NIH) that aim to develop image computing and machine learning methods, together with translational tools, to understand
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include PhD dissertation and publications. Experience in Artificial Intelligence/Machine Learning and have a strong programming background in Python. Experience working on research projects in an academic
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biological context and interpretation to characterize chemical mechanism of action; (4) apply machine learning algorithms for identification of biomarkers and classification of environmental toxicants; (5
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limited to: Collect data from a range of in vivo physiological and behavioral studies predominantly in genetic mouse models, learn a variety of surgeries, learn gold standard methodologies to study glucose
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meetings, present at research meetings, journal clubs, lab meetings (10%) Teach/supervise students, professional research assistants, and other lab members (10%) Developing grant-writing skills by applying
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early intervention are encouraged to apply. Key Responsibilities: Acquire an understanding of the roles of various disciplines that serve individuals with developmental disabilities and their families