115 algorithm-phd-"Prof"-"Washington-University-in-St"-"Prof" positions at University of Pittsburgh
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learning algorithms focused on image data, natural language processing, and tabular data Conduct exploratory data analysis and feature engineering for high-dimensional datasets, including image, text, and
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revealing mechanisms and therapeutic targets of early pre-malignant development of ovarian carcinoma, novel big data approaches, and development of early detection/screening tests/algorithms of ovarian cancer
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of Pittsburgh. This Lab examines the intersection of public health, epidemiology, and ophthalmology. The candidate should have a PhD in a statistics-related field and at least one year of experience in analyzing
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with Dr. James Cook. This position offers an opportunity to collaborate in delivering contemporary perspectives on Asian cultures, societies, and issues to undergraduate students. Requirements: PhD in
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(engineering.pitt.edu/bioengineering) invites applications from accomplished individuals with a PhD or equivalent degree in bioengineering, biomedical engineering, or closely related disciplines for a non-tenure stream
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Professor, non-tenure stream position. Main responsibilities include: Clinical trial methodologies Study design Data analysis Minimum experience: PhD in Biostatistics with 10+ years within a Scientific
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Python Proficiency: Demonstrate expertise in Python or a similar high-level programming language is essential for developing algorithms and backend logic Azure DevOps Experience: Familiarity with Azure
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materials, implementing algorithms, creating software, and investigating new approaches and technologies. Assists with conducting research, including setting up, calibrating, maintaining research records
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medical schools in the U.S. and is located in one of America’s most livable cities. Requirements for Assistant Professor include a PhD or equivalent degree in the field of Microbiology or Immunology, 3
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to create analytic data cohorts (study cohorts). Apply machine learning methods on clinical datasets to identify predictive factors – selecting algorithms, preprocessing data, training models, and evaluating