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familiarity with supercomputing or cloud platforms. Experience with AI/deep learning beyond simple tools (e.g., Random Forest, ANN), particularly in integrating physical models and AI algorithms. Knowledge
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for recording across multiple cortical/ striatal circuits •Optogenetic and electrical real-time perturbations, including interventions directly related to clinical deep brain stimulation. •Parametric
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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gene expression analysis to study animal models and human immune cells. We are currently exploring the biology of tolerance versus allergy to foreign proteins in animal models and developing a deep
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interest in the use of machine learning techniques to enable new analysis strategies, as well as the application of deep understanding of the detector to enable novel physics studies. The group also has a
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health-related scientific questions Preferred Qualifications: ● Research experience in predictive modeling and/or longitudinal datasets ● Expertise in deep learning, reinforcement learning, transfer