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modeling, physiological signal analysis, and innovative neuromodulation strategies for neurological disorders such as epilepsy, chronic pain, and autonomic dysfunctions. Primary Responsibilities 35
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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-tuning DNA sequence/genomic/omic large language models (gLLMs) with their applications to genetics, e.g. in identifying causal genes for Alzheimer’s disease (AD). You will have access to state-of-the-art
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flux measurements using biogeochemical modeling. They will be responsible for managing projects related to field instrumentation and ecosystem flux modeling. 20% designing and implementing trace gas flux
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quality models to better understand the effect of soil health management practices on soil-water storage in agricultural fields. A postdoctoral position is available to examine the effect of soil health
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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and then model their space use and behavioral patterns. The post-doctoral researcher will also be responsible for coordinating a team to deploy and monitor behavioral playback cameras, developing a data
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or more of the following: ● Experience with urban watershed modeling or lake systems modeling ● Experience with limnological or aquatic field methods ● Experience with statistical methods for making
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analysis of data including measures of pupil dilation, microsaccades, and behavioral measures of speech perception. Experience with data collection and statistical modeling of time-series data are essential
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, Molecular Biology, or a closely related biomedical field • Experience with retinal immunopathology, photoreceptor biology, or RPE-related degenerative disease models • Demonstrated expertise in retinal