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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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help develop and characterise advanced patient-derived tumour models and use them to test promising therapeutic targets that exploit vulnerabilities caused by loss of the SMARCB1 gene. This role offers
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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Agency (ARIA). The PROTECT project (Probabilistic Forecasting of Climate Tipping Points) brings together cutting-edge AI, statistical, and machine learning techniques with climate modelling, aiming
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samples and disease models. Working closely with a dynamic and multidisciplinary team of clinicians and scientists, you will help generate and interpret high-resolution datasets that reveal new insights
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experimentation, applying state-of-the-art single-cell multiomic approaches and functional genomic screens to patient-derived samples and disease models. Working closely with a dynamic and multidisciplinary team
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will develop novel tools which will allow efficient flow modelling tools for other researchers to explore higher fidelity thermochemistry modelling. The main responsibilities of the post will be
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Nuffield Department of Clinical Neurosciences (NDCN), MRC Brain Network Dynamics Unit, Mansfield Road, Oxford The post holder will develop computational models of learning processes in cortical
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical