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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
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science, artificial intelligence, machine learning, computational social science, data science, or a related computational discipline. Applicants must have experience with digital trace/multimedia datasets and in
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic
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Principal Investigator and a cell-culture specialist in a friendly, multidisciplinary group spanning optics, electrophysiology, microfabrication and machine learning, collaborating with partners
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denoising, cell segmentation and the analysis of cellular neighbourhoods and cell–cell interactions. Experience in developing artificial intelligence and machine-learning methodologies for multimodal data
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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 approaches to large-scale spatial genomics
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hours in the space allocated to the group in London (fully remote work is not possible). The postholder can expect: Provision of a quiet work space, a computer, access to high-performance computing, a lab