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Institute. Working closely with the Lotfollahi Lab – leaders in generative AI and foundation models for spatial and single-cell genomics – you will develop and apply state-of-the-art machine learning
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geophysical investigation, geomechanical engineering, and machine learning. The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space
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regulatory environment, and documenting insights that can inform Agency-wide AI governance and implementation efforts. You will apply machine learning methods and applied AI tools, including experience with
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areas of environmental change, remote sensing, infectious disease surveillance, spatial epidemiology, machine learning and global health. Key responsibilities: - Conduct high quality research in
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the supervision of the Principal Investigator, including but not limited to the following: Develop new computational tools through the application of AI / deep learning / machine learning / statistics on spatial
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materials discovery with cutting-edge high-throughput platforms, robotics, machine learning, and autonomous experimental workflows. Access World-Class Facilities: Based in the Department of Chemical and
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machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization—to identify
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strong international track record in content provenance and authenticity research spanning computer vision, watermarking, machine learning, privacy-preserving technologies and open standards. We have
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Intelligence & Machine Learning Develop machine learning and AI models to identify, predict and characterise genome instability patterns. Design generative and predictive computational models to infer mutational
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and machine learning algorithms to analyse and interpret the acquired data. The successful candidate will work primarily with Dr Qimei Zhang in the Department of Engineering at the Nottingham Trent