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Field
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and advanced causal inference methods to large-scale multi-omics datasets, national health registers, and other comprehensive health-related data sources. Duties The doctoral position is intended
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. This project aims to dive into the dynamics of attack methodologies (e.g., Membership Inference, Property Inference) and defensive mechanisms (e.g., Differential Privacy, Machine Unlearning) within FL
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prehistoric individuals, to make inferences about demographic history, migration and admixture patterns, and signals of adaptation. The project will include computational analysis of archaic introgression and
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involve EEG, eye-tracking, or applied fieldwork, depending on interest and expertise. While this is not a confirmed PhD place, a small number of outstanding candidates will be supported to develop a strong
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motion) Pose tracking and behavior segmentation with tools like DeepLabCut, MoSeq, and Kinect-based systems Longitudinal analysis of behavior from early postnatal to adolescent stages in
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motion) Pose tracking and behavior segmentation with tools like DeepLabCut, MoSeq, and Kinect-based systems Longitudinal analysis of behavior from early postnatal to adolescent stages in
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electron microscopy image simulations Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images Application of the new approach
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to co-design algorithms and circuits to develop efficient neuromorphic hardware, tailored to target tasks. In detail, you will: develop circuit-plausible training/inference algorithms and analyze in
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Background Network Rail operates several telecom networks which provide connectivity for various signalling systems. Therefore, the performance of telecoms assets is integral to how the railway system operates
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to measure how young brains track information content within screen media in real-time. The project will involve collecting and analysing EEG, fNIRS and home wearable recordings from babies and children