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Field
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across tasks; Structured and compositional learning; Vision-language and multimodal models; Learning with limited supervision; Generalisation beyond pretraining distributions The precise research agenda
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to distributed execution, checkpointing, filesystem performance, networking, and GPU utilisation Help maintain reproducible training recipes, configuration files, launch scripts, and documentation Work with
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essential for learning complex distributions over structured data such as text, graphs, and biological sequences. Developing and understanding models for dis- crete spaces is therefore a key challenge in
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chains and intermodal distribution networks, exploring synchromodal strategies to enhance the efficiency, flexibility, and resilience of the chains.; As part of the fellowship, the selected candidate will
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causal inference in heterogeneous data environments, addressing the challenge of enabling trustworthy causal analysis across distributed datasets while preserving privacy. The successful candidate will be
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to research, develop, and evaluate algorithms for a variety of signal and/or image processing applications. The successful candidate will have a solid background in computer science with strong skills in
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on designing novel ultrasound data acquisition and processing algorithms with both the standard Clarius ultrasound system and the more advanced Sonus Microsystems research ultrasound system. This includes
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Overview We are seeking an experienced researcher to develop decentralised swarm mapping and localisation algorithms for underground pipe operations as part of an international research programme
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a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and
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statistics and data science -- including artificial intelligence -- the community pioneers emerging fields, develops the algorithms that power the information age, and is committed to creatively and