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Field
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for imaging • Genetics • Multivariate methods • Causal inference • Computational neuroscience • Ecological momentary assessment • Geospatial data analysis • Randomized clinical trials How
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mass spectrometry; and advanced chemometric tools for the analysis, interpretation, and comparison of complex metabolomic data sets. https://www.nist.gov/programs-projects/metabolomics-quality-assurance
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, Spectronaut, and/or DIA-NN, and with applying statistical and multivariate analysis methods to large-scale proteomics datasets; programming proficiency in R, Python, or C++ is a plus ● Skilled in sample
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Social Neuroscience lab (https://www.msn-lab.com) led by Prof. Matthew Apps. The lab studies the cognitive, computational and neurobiological basis of motivation and decision-making in healthy young adults
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(i.e., scheduling, record-keeping, etc.) and interacting with human subjects Manage data analysis, brain imaging, multivariate image analysis Help manage undergraduate research assistants Other duties as
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patient-facing study materials and information; conducting data collection; participating in community engagement activities’ completing data cleaning, descriptive analyses, and multivariable analyses
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modeling and statistical analyses in collaboration with project principal investigator and team’s PhD-level biostatistician, including descriptive and multivariable statistical analyses. Performs analyses
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analysis Multivariate statistical methods Longitudinal data analysis Causal inference methods Demonstrated ability to select and apply appropriate statistical methodologies to complex clinical, public health
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the COST Action InfoGUT project (https://infogut.eu/ ). • PI's UTU profile: https://www.utu.fi/en/people/matti-ruuskanen • PI's Google Scholar profile: https://scholar.google.com/citations?user=anXmb6IAAAAJ
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Familiarity with brain imaging analysis acquisition Knowledge of multivariate statistics and/or digital signal processing Proficiency in Python. Parallel computing processing experience is highly appreciated