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. This position offers the opportunity to lead impactful research at the interface of aging biology, neurodegeneration, and spatial omics. The successful candidate will contribute to high-profile projects
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paradigms; cognitive, brain-inspired and neuromorphic computing, human-machine interface technologies; advanced heterogenous architectures based on integrated commodity components, special purpose
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sequencing, bulk RNA-seq, and single-nucleus RNA/ATAC using R, python, command line interface and high-performance computing clusters Thorough documentation and communication of experiments and findings with
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Optical see-through head-up and
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constraints. The postdoc will work at the interface of reinforcement learning and computational epidemiology, focusing on the development of new reinforcement learning algorithms. The project will consider
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duties or projects as assigned. Knowledges, Skills, and Abilities: Technological knowledge base in graphic design, computer programming and/or user interface design. Skilled in developing web-based
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tools for interfacing with the brain, including genetically encoded voltage indicators (GEVIs), optogenetic silencers, and fluorescent proteins. We welcome candidates with diverse expertise, which may
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the opportunity to contribute to an internationally competitive research programme at the interface of proteomics technology development, regulatory protein biology, and cancer research. Expected start date and
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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
disrupted by neurological disorders and injuries. The lab uses high-density neurophysiology (Neuropixels), high-density spinal stimulation, brain-spine interfaces, computational modeling, computational