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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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on multiscale subsurface imaging using advanced geophysical imaging techniques and on investigating how subsurface structure, material composition, and geodynamic processes influence regional seismicity. Key
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– materials and processes for syngas upgrading (e.g., tar, chlorine, sulfur removal); (c) Development of new materials from waste, e.g. slag in construction, nanomaterials for batteries and plastic additives
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to identify perturbed pathways, molecular signatures and clinically relevant biomarkers Contribute to translational research activities, including processing and analysis of clinical specimens from patient
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research reports) and academic publication processes will be preferred. Knowledge Good knowledge of cognitive functions and cognitive neuroscience theories and techniques. Good or working knowledge
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machine learning, computer vision, and medical image analysis, with publications in top-tier AI and medical image analysis conferences and journals, including CVPR, ICCV, ECCV, NeurIPS, MICCAI, TPAMI, TIP
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conversion layers. The successful candidate will develop robust PQD/polymer composite films, optimize scalable film fabrication processes, and perform comprehensive optical and structural characterization. Key
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. Support centre-level processes and reporting by collating information and materials required for submissions to funders, government agencies and other stakeholders. Any other research-related
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findings (e.g., conference presentations and research reports) and academic publication processes will be preferred. Knowledge Good knowledge of cognitive functions and cognitive neuroscience theories and
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research reports) and academic publication processes will be preferred. Knowledge Good knowledge of cognitive functions and cognitive neuroscience theories and techniques. Good or working knowledge