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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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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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across time and contexts. Job Description: You will develop and apply mathematical models and machine learning algorithms to analyze the structure and evolution of knowledge systems across different
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a Distributed Hydrology Soil Vegetation Model (DHSVM), produce calibrated simulations, and validate the model for the MEF. The research includes: empirical investigation of high-resolution soil data
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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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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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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