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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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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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focused on understanding and countering harmful narratives and, mis/disinformation, and applying social network analysis. To be successful you will need: PhD in a relevant discipline such as computer
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fresh perspective on how specialized brain networks can identify and categorize causes of sensory inputs, integrate information with other networks, and adapt to new stimuli. It proposes that perception
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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target trials, group-based trajectory modelling, mediation analyses, and Bayesian modelling approaches to investigate adherence to cancer treatment and recurrence. The research fellow will join a vibrant
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Summary Research in the Ichinose Lab is in the field of vision and neuroscience. They use the mouse retina as a model system to investigate neural networks and physiological functions and have the
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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