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opportunities for collaboration with Michigan State University, and IU’s network in cognitive modeling, AI, and human–AI decision research. This postdoctoral appointment is full-time and on-campus. Job Duties 80
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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generation of health data scientists. Areas of expertise include bioinformatics, computational biology, artificial intelligence, network science, Bayesian methods, spatiotemporal methods, visualization
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle
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new era in the exploration of the universe. The addition of the Virgo antenna into the network led in 2017 to the detection of a neutron star binary merger that could be followed in electromagnetic
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), and physiological parameters in the study of animal behaviour; a strong background in data analysis using R, preferably experience with Bayesian statistics and social network analysis; lab experience
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear