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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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Argonne National Laboratory is seeking a postdoctoral researcher with expertise in life-cycle analysis, industrial process modeling, and critical minerals and materials assessment. The appointee
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about understanding how people perceive, move and make decisions in complex and uncertain environments? Do you enjoy combining experimental research with computational modelling to tackle fundamental
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analysis, model development, and signal processing techniques for seabed characterization. The position is budgeted for an initial two-year period and may transition to a full-time Research Scientist
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: to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data and crop models, we aim to enable more precise and
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-associated virus (AAV)-based gene therapy treatments for inherited retinal disease (IRD). The successful candidate will generate translational research data required to support the preclinical development
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-mail) Does this position pay above the required minimum?: No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through
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infection in vulnerable infants? Join an NWO Vidi project that combines advanced gut-on-a-chip technology, organoid models and microbiome approaches. You will work in an interdisciplinary setting to establish
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/Python), deployment (to the agents' onboard computational hardware using the Robot Operating System), and experimental testing (on unmanned vehicle swarms). A solid competency with the standard
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
Hill seeks a computational postdoctoral researcher to develop and apply methods for integrating single-cell sequencing and imaging data within controlled cell-line model systems. The project focuses