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Field
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research. The postdoc will lead one or more discrete projects with a clear set of research objectives that will result in first-authored publications. In addition, we expect the postdoc to develop additional
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the project lead peer reviewed outputs relevant to acoustic tracking and project modelling objectives, and other objectives as appropriate. About you The University values courage and creativity; openness 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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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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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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) or other expertise relevant to the project objectives (see above) Early career track record including publications or other outputs in the related field In case your background is in psychology or medicine
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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
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in
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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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-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