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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 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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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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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
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on the willingness to fight. This postdoc position is part of the project “What are Citizens Prepared to Fight for? The Netherlands in Comparative Perspective”. The overall objective of the project is to gain a deeper
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), and sustainable chemistry. In particular, the role will involve: (i) designing blockchain architectures and smart contracts for CO2 credit tracking, tokenisation of CO2 derived products; (ii