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Field
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Job Description This position offers a unique opportunity to work on advanced projects at the intersection of AI, machine learning, computer vision, and multimodal learning, focusing on advancing
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: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing Good communication skills and an appropriate publication record are essential. Solid knowledge of Python and C++ is
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datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding of malignant
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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relations and in interaction with organizations, to gain insight into computer-mediated communication, its applications and effects, and to pursue evidence-based communication strategies, instruments and
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL
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applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers). * except for RF payloads. ** including artificial intelligence and machine learning
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), electrophysiology, genotyping, brain stimulation (tES, TMS), computational modeling and/or machine learning. For all our projects, we seek post-doctoral researchers who aim to take leading roles in projects
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professional deliverables ● Experience with causal inference, machine learning, and artificial intelligence is desirable ● Experience with clinical, EHR, or biobank data analyses is desirable
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g