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Field
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discipline knowledge and developments. Demonstrated ability to undertake high quality academic research and conduct independent research with limited supervision. Demonstrated track record of publications and
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settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on obtaining external funding and project
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. We take a bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications. Job description Track 1
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assessment models, and statistical modeling in both frequentist and Bayesian frameworks • A solid track record of publications Appointment Type Restricted Salary Information Commensuate with experience Review
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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Ability to acquire new skills beyond current expertise to achieve project objectives. In-depth theoretical and practical knowledge in engineering or cell biology. Ability to work independently and
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work and how they might be misused or abused (both intentionally and unintentionally) Boundary objects that can be used to bridge the divide between educators and AI experts (e.g. causal DAGs, hybrid
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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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critical questions about the ways in which AI tools work and how they might be misused or abused (both intentionally and unintentionally) Boundary objects that can be used to bridge the divide between
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(e.g. Python, R) 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