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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris
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, molecular diagnostics and novel treatments of lung diseases. The Centre offers a vibrant, inclusive, collaborative, and interactive environment. The postholder will interact with clinicians based at the Royal
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, molecular diagnostics and novel treatments of lung diseases. The Centre offers a vibrant, inclusive, collaborative, and interactive environment. The postholder will interact with clinicians based at the Royal
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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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for energy efficient hardware such as RISC-V The opportunity to interact with high performance computing companies like NVIDIA, XILINK (AMD) and HPE The opportunity to contribute to Xcompact3d, an open source
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for medicine use before and during pregnancy. This postholder would work primarily on a recently funded programme of work to develop a novel approach to understanding and communicating the Safety of Medicines in
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Institute for Molecular and Computational Medicine (IMCM). You will test GSK assets and targets in established models of podocyte and mesangial cell pathology relevant to glomerular diseases. You will
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connectome, with a focus on the chemosensory circuits involved in human host-seeking. The principal focus will be on the high level proofreading, annotation and analysis of connectomics data. This will include
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Mathematics), the post holder will interact with researchers in statistics and machine learning at Royal Holloway, the Biophotonics Group at the University of Nottingham, clinicians at the Nottingham Breast
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human