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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 36 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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cutting-edge machine learning methods for spatial omics and for multi-modal data integration. The post-holder will also collaborate on the development of new computational methods to support the CoRE’s
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an expert in the use of advanced statistical inference and machine learning methods for the analysis of omics datasets. You will work with a team of data scientists in Manchester and Oxford to create tissue
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the centre. Successful applicants will have (or be about to obtain) a PhD in computational biology or computer science, with knowledge of multimodal spatial omics data integration and machine learning
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Join our dynamic, multidisciplinary team as a Research Associate and make a transformative impact in scientific machine learning and digital twins for healthcare innovation! This role focuses
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and enabling commercial deployment. The work will integrate model based design of experiments, machine learning, hybrid and kinetic modelling (digital twin development), process design, simulation and
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behavioural, physiological, and environmental acoustic variables. This post would suit someone with a PhD in biomedical engineering, data science, machine learning, experimental psychology, audiology, cognitive
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opportunity to work at the intersection of multimodal AI, cancer biology and digital pathology. The successful applicant will have a PhD or MSc with a significant machine learning element and/or experience with
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, and passively collected data such as geolocation. The model will be dynamic (updating predictions as patients record new data), and will consider statistical and machine learning algorithms. Development