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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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position is supported by a National Institute on Drug Abuse-funded R01 award. The position involves contributing to a dynamic team engaged in developing and applying a combination of statistical analyses and
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desirable For computationally focused candidates: proficiency in a scripting language (e.g., Python, R) and experience analyzing next-generation sequencing data; experience with statistical modeling
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Qualifications: A PhD, MD/PhD, or equivalent research doctoral degree in neuroscience, biomedical data science, computer science, psychology, psychiatry, statistics, engineering, applied mathematics, or a related
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statistics, biostatistics, computer science, computational biology, or a closely related field and have demonstrated interest and expertise in causal inference and clinical trial design. Required
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. The successful applicant will have experience with EEG analysis, a strong statistical analysis background, excellent interpersonal skills, and strong scientific writing abilities. Since our work is multi
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-series data, or other high-dimensional biomedical datasets. Strong foundation in signal processing, statistical analysis, computational modeling, or algorithm development. Ability to process, clean
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biology and offer training in human histology, statistics, omics data analysis, computer vision, grant writing, and scientific publishing. Required Qualifications: 1. A doctoral degree (PhD, MD
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Qualifications: PhD in a relevant field is required; relevant fields include but are not limited to epidemiology, statistics, biostatistics, machine learning, data science, or other quantitative fields. Required
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graduate-level knowledge of spatial analysis, machine learning, large dataset management, and statistics or econometrics. Applicants must be proficient in R and/or Python. Advanced knowledge of R's and/or