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Field
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard
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graph neural networks for complex sensor networks such as those involved in brain imaging Develop and test data-driven methods for image and video processing for microendoscopy. Key Duties and
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. Job Description: A postdoctoral position is available in the laboratory of Catherine Marcinkiewcz, Ph.D. at the University of Florida. Research in the Marcinkiewcz lab focuses on unraveling neural
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human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus
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of neuronal loss to better understand why neurons die or axons are damaged to ultimately establish new strategies for the preservation or restoration of neural tissue. We use multiple approaches, but focus
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platforms, as well as extensive networking opportunities within the University of Miami’s robust AI and digital health ecosystem. Program Objectives: Provide fundamental training by interdisciplinary faculty
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neural network models, produce stimuli for artificial and biological agents, participate in experiments with chicks maintained in the Biological Services Unit, contribute to lab meetings and research
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techniques including deep neural networks and large language models (such as GPTs), with state-of-the-art functional genomics approaches, including crop genomics, genome editing, and single cell multiomics
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work on a study funded by the National Institute of Mental Health. The major goal of the project is to use a combination of modern circuit neuroscience technologies to establish neural circuits