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Field
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, statistical inference and machine learning, with potential applications to practical problems Independent teaching Supporting research work, participation in research projects, and assistance with teaching and
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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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, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. About the project The role of the PhD candidate will
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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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Infrastructure? No Offer Description Start date/duration as soon as possible until 14.09.2027 Job profile: Your responsibilities: PhD thesis in the field of software engineering and model-driven engineering
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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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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probabilistic modelling, latent-variable methods, and structured unsupervised learning. Research Environment & Collaboration The successful candidate will work at the interface of probabilistic machine learning
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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