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Field
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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Developing and validating 3D-OCT-based tools for posterior eye shape characterisation Applications are invited for a fully funded three-year PhD studentship sponsored by Carl Zeiss AG. The
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candidate for a full-time (100%) PhD position for 3 years. You will join the research group Power Electronics and Electrical Machines (PEM) at IEL, where we foster an open, inclusive, and collaborative
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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PhD fellowship in fault tolerant quantum algorithms PhD Project in state preparation, observable extraction or noise modelling Niels Bohr Institute Faculty of Science University of Copenhagen
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vision, audio analysis and explainable AI methods. Rather than assuming that behavioural signals reveal personality, deception or suitability, the research will test whether any signals provide reliable
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps