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innovative statistical methods in clinical trial design and variable selection methods in high dimensional data that will predict clinical outcomes and meta-analyses. The successful candidate will collaborate
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, you will focus on delivery methods for genome and epigenome editing tools. This will require expertise in molecular and cellular biology, molecular engineering, genetic manipulation, and bioinformatics
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, mood disorder models, or equivalent. The candidate should also have proficiency in some surgical neural targeting method. Programming skills and an understanding of mouse genetics are also a plus. The
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functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
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analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET). The candidate will contribute to the design, development, and
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animal species, generating standardized data that works effectively across diverse languages and cultural contexts while eliminating traditional barriers of recall bias. These methods are being deployed in
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utilize new machine learning methods for 3D behavior tracking and analysis. · Advise PhD students on related projects. Other Work Performed and Expectations · Document progress consistently and
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. Ability to execute appropriate computational and statistical analyses and present data in 5. Abililty to learn from existing literature to develop new methods and strategies to analyze complex datesets. 6