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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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of diversity in the hyper-diverse arthropod clade Coleoptera (beetles). Our research includes multidisciplinary approaches encompassing phylogenomics, morphology, ecological, and distributional data. The Insect
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and
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, and publication of major results from the experiment. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS C
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development, and disseminate results at conferences. This position will work Monday-Friday with weekends as needed. Expected distribution of duties includes: ● 75%: Laboratory benchwork ● 25%: Data analysis
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the fate and distribution of contaminants in the environment. The researcher will be directly supervised by PI Cara Santelli, who has a diverse lab that is committed to inclusivity and creating a sense of
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. Expected distribution of duties includes: ● Laboratory benchwork: 75% ● Data analysis, writing, and presentations: 25% Qualifications Required Qualifications: ● A PhD degree in Neuroscience or a related