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outcomes. The individual will be expected to develop stimulation strategies and testing algorithms, write code, and develop software. They will do extensive validation and testing, under the supervision
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validate signal processing algorithms and stimulation strategies using electrophysiological and behavioral data. Develop GUIs, psychophysical test protocols, and objective outcome measures (e.g., ECAP, ABR
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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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relevant academic field(s) is also required as is the ability to mentor students and work in a diverse, distributed team in an interdisciplinary manner with an ability to direct one’s own research. Preferred
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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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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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, 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