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Field
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on text and image feature learning for news ecosystems, analysing the complex multidimensional feature space of visual information to support data-driven journalism. This includes experiments
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Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
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physicians. Career opportunities upon completion of the postdoctoral training include: Clinical medical physics Medical imaging computer programming Radiation therapy computer programming As a successful
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, or a related STEM field. Experience with EHR data, machine learning or deep learning, natural language processing, medical imaging, or large language models (LLMs) is highly desirable. Familiarity with
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Location: The Francis Crick Institute, London Short summary We are seeking an ambitious Postdoctoral Fellow to develop the next generation of deep mechanistic models (DMMs; Fabrini & Fröhlich
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, social learning, intrinsic activity, and predictive coding. We are now seeking highly motivated and enthusiastic postdoctoral fellows (project researchers) or project faculty members to work under the
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for supervised and unsupervised learning. We devise deep learning models, which find application in imaging and image-based science, including in collaboration with domain scientists on and off campus in fields
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background Recent work has unsettled the layered picture of sub-Neptune interiors. Evidence for extensive miscibility between metal, silicates and hydrogen and for the strong coupling between deep interiors
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with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as
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but are not limited to modern survival analysis, high-dimensional statistical inference, AI and machine learning, deep learning and its statistical foundations, causal inference, statistical methods