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Field
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deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine which features are relevant? These questions
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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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, you will: Acquire and analyse human neuroimaging data, with a primary focus on high-field fMRI of natural sound perception. Develop and apply AI/NeuroAI models, including deep neural networks, to model
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, Computational Neuroscience, Computational Psychology or Behavioural Science; Transport Modelling, Transportation Science or Urban Mobility; Data Science, Artificial Intelligence, Machine Learning
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates