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with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large
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programming (e.g., Python/C++), machine learning frameworks, or robotics software environments such as ROS. You are motivated to work in a multi-disciplinary research environment combining engineering, AI, and
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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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languages and environments. Experience with machine learning and AI in biomedical settings would be considered favourable. You will also need a problem-solving ability to manage and analyse datasets and the
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with expertise in areas such as Machine Learning and Deep Learning, Generative AI, Data-Centric and Responsible AI, or AI for real-world applications (e.g., health, cybersecurity, robotics, or business
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foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands
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, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing
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language processing, statistical machine learning and causal inference. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional
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for the AiTASHA project. We welcome applications from candidates who: Have research expertise in statistics with a strong mathematical foundation. Have experience in AI, machine learning or deep learning, and
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The Medicines And Healthcare Products Regulatory Agency; | Canary Wharf, England | United Kingdom | 23 days ago
machine learning where appropriate. The successful candidate will have: A PhD in Data Science, Statistics, Machine Learning, Computer Science or a similar quantitative discipline or demonstrable equivalent