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time of appointment. Demonstrated expertise in bioinformatics, statistics, microbiome data analysis, and/or computational methods. Experience working with large population-level datasets (e.g., NHANES
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transcriptomic data. • Detect and interpret structural variation from Nanopore/PacBio sequencing. • Build scalable, reproducible pipelines for large genome collections and public databases. • Collaborate closely
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of large data sets. Determining fundamental and technical limits of a measurement, using principles such as the Cramer Rao bound and Fisher information, Gaussian process, Kalman filter, and state estimation
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traits. A NIDA-funded study concentrates on cannabis use genetics and PRS prediction of response to THC in a laboratory paradigm, and an NIMH study focuses on depression and anxiety genetics in large
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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technologies to analyze the molecular heterogeneity and connectivity of spinal neurons, explore how descending systems in the brain convey information to the spinal motor system, and define the functional roles
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(Thorlabs) for imaging large cortical and subcortical areas in awake head-fixed animals, two 2-photon microscopes for single-cell electrophysiological experiments in brain slices (Chun et al, Science 2014
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mechanisms – cellular, synaptic, and/or systemic – that engender anhedonia. Leveraging patient-derived data to corroborate mechanistic insights is highly encouraged. The major objectives of the project
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increasing independence over time. Collaborate on project and analysis design guided by their PI. Develop new computational methods. Adhere to field and lab standards for data analysis. Identify, process
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with large data sets Good programming skills Excellent communication and interpersonal skills Very good command of English Experience in writing scientific publications Knowledge of the German language