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Field
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) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting. Previous experience and
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closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related domains Hands-on experience with robotic hardware (e.g., robot
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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object
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Google Earth Engin, R, Python, and STAN (e.g., deep learning, Bayesian regression models, spatial analyses), and running analyses on a high-performance computing cluster. Demonstrated record of publishing
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
completed prior to starting the traineeship. Required Qualifications, Competencies, and Experience Candidates must have completed, or be on track for completing, a doctoral degree. Interests and expertise
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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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to our work, meaning that we set up experiments in the lab or online to measure behavioural performance (e.g. response times and accuracy) to tasks involving object recognition and categorisation. Our
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate
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communication and collaboration skills Preferred: Experience with simulation-based inference and Bayesian methods Familiarity with cosmological simulations or observational cosmology ML architecture design and
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transportation challenges and opportunities with innovation, objectivity, and unmatched technical expertise. Our staff delivers excellence, value and thought leadership to ensure our research sponsors achieve