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University of California San Francisco | San Francisco, California | United States | about 2 months ago
anomalies, and work to continuously improve data quality. * Provide technical expertise in data storage structures, data mining, and data cleansing. * Lead and participate in dissemination of best practice
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, software development, knowledge representation, machine learning, text mining). Relevant data analysis and programming skills (e.g. Python, R) are required. This is a full-time post (35 hours per week), and
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using a variety of data extraction tools (e.g., web-scraping, data mining). S/he will independently carry out data management, visualization, wrangling, and analysis tasks. The Data Scientist will assist
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, Data Mining, and Artificial Intelligence. The typical teaching load is four courses per semester. Key responsibilities include: Course/curriculum development Student advising Industry outreach Student
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(including service-connected disabilities), gender (including pregnancy related conditions), military status or military obligations, sexual orientation, gender identity or expression, genetic information
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of technical and analytical experience developing data models, data mining and segmentation techniques. Preferred Qualifications: Master's degree in Computer Science. Progressive experience in general technology
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dimensionality reduction. Application of machine learning in drug discovery: target identification, drug repurposing, virtual screening, PK/PD modeling, and drug-drug interaction prediction. Data mining
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into protein databases (FASTA) to be used in mining raw Mass Spectrometry Proteomics data. The aim is to generate novel knowledge about the tumor biology, discover tumor antigens, and associate the findings with
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. Knowledge of advanced statistical analysis (e.g., regression, predictive modeling), machine learning techniques (e.g., clustering, random forests, etc.), and/or data mining. Preferred Qualifications
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it better. Essential Duties: To identify new sources of data and develop methods to improve data mining, analysis, and reporting. To present the findings in reports (in table, chart, or graph format