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will play a pivotal role in strategic analytics initiatives, people research, and experimentation. You will design and implement advanced statistical models, algorithms, and data visualization tools
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and publication. Preferred Education, Experience and Skills: A master’s degree or a PhD in computer science, bioinformatics, mathematics, molecular biology, statistics, or a related field is preferred
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, clinical scientists, biostatisticians, and pharmacokineticists to interpret study results and ensure study results and statistical interpretations are accurately and clearly reflected in relevant documents
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position with experience in physiologic signal processing (e.g., EEG) and statistical/machine learning methods. The applicant would be joining the lab of Dr. Jennifer A. Kim MD-PhD, which focuses
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of their PhD). Appointments are available to start in Winter 2024 (January 1, 2024 or sooner). This is a multi-year position for individuals seeking to become independent scientists in: (a) Digital psychiatry
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and perform hands on experiments and maintain detailed documentation Qualifications: Must have a PhD in a field applicable to cancer research and associated experience in a health-related area such as
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, the inaugural Senior Associate Dean of Public Health Data Science and Data Equity, Anna M.R. Lauder endowed Professor of Biostatistics, Professor of Chronic Disease Epidemiology, Professor of Statistics and Data
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development, data verification and oversees compliance with information security etc. 3. Designs data architecture, establishes data dictionaries, uses statistical programming and transitions old databases
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, there are multiple opportunities for translational research. Qualifications: PhD, MD or MD/PhD in neuroscience, neuroimaging, statistics, psychology, engineering or a related field is required. Previous experience
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/ Qualifications: Qualifications required are a PhD in statistics or biostatistics or relevant areas, a strong background in statistical methods and programming, and good writing and communication skills. Prior