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-time (1.0 FTE) Postdoctoral Fellow with a strong background in data science and machine learning. Experience with pediatric cancer and a desire to become a pediatric cancer researcher is highly desirable
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perioperative medical education. We are dedicated to developing leaders in anesthesiology and perioperative medicine who demonstrate clinical excellence, professionalism, and a commitment to life-long learning
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, Machine learning, Biomedical Informatics or related fields Preferred Qualifications: Experience in developing and training NLP/deep learning models on GPUs (with framework such as PyTorch, tensorflow
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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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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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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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, 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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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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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