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that capture ILC-specific morphology and biology while remaining robust to differences between hospitals, scanners, staining procedures and protocols. The researcher will investigate self-supervised and
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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management of juvenile fish habitats, the student will be trained in a range of inter-disciplinary skills including coastal fish sampling, scientific diving, digital technologies and computer vision. Would
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application! We are now looking for 1–2 PhD students for the Division of Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Within the research unit
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal microscopy and SEM, with computational image analysis, computer vision and machine
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archaeologists to understand AI results – Generalization and transferability analyses, considering domain adaptation and transfer learning strategies to ensure model robustness across different geographic regions
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
of these to explore how different configurations of human-agent teams affect human situation awareness, decision making, and task performance. We are looking for a highly talented and dedicated PhD student with a 1st
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join the internationally recognised EnDROIDS programme (https://endroids.ico2s.org ) and become part of a highly interdisciplinary team of computer scientists, molecular biologists, DNA nanotechnologists
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uncertainty, detect errors, and regulate their behavior accordingly. The PhD project will examine how metacognitive monitoring and regulation develop across different age groups and within individuals over time