16 phd-machine-learning "https:" "Computer Vision Center" Postdoctoral positions in United States
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interdisciplinary professional partners. Other duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong
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interdisciplinary professional partners. Other duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong
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structure of withdrawal, (2) acquire data from genetically diverse mice under a multitude of conditions, and (3) develop multimodal machine learning models and methods to determine signatures and
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top-tier venues and will have dedicated time for advancing their own research. The project is in collaboration with CCL (The Center for Connected Learning and Computer-based Modeling, PI Uri Wilensky
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- Epigenomics, Machine Learning & 3D Genome Biology Employer NYU Langone Health, Skok Lab Location New York City, New York (US) Salary Postdoc: $70,000+ annually, plus benefits; Senior Computational Biologist
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year’s search is on faculty with expertise in Quantum Computing with particular focus on Quantum Algorithms, Quantum Artificial Intelligence/Machine Learning, and/or Quantum Security. The successful
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large-scale models Expertise in at least one of computer vision, speech recognition, or multimodal learning, with experience in real-world technology deployment Experience with human-in-the-loop
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
analytic models of infectious disease and will use dynamic models and/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
, University of North Carolina at Chapel Hill ( UNC -CH). We seek a motivated candidate with a strong interest in machine learning and clinical neuroscience. The lab studies resilience and vulnerability in
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., constructs, sequences, models, and biophysical data (LIMS) Assisting in the development of new predictive models based on the gathered experimental and computational data using machine learning methods