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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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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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leading to cardiomyopathy. This work will be performed in primary cardiomyocytes from human iPS cells, human myocardial slices from patients and humanised mouse models with the same mutations as patients
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blades. In this role you will: Design, build and optimise optical and inductive thermal NDE rigs for curved, metre‑scale blade sections; Develop and validate forward–inverse heat‑transfer models
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humanised mouse models with the same mutations as patients, all of which are currently available in the laboratory. Minimum qualifications are PhD and/or MD with expertise in molecular and cellular biology
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(multivariate classification and/or computational modelling) to carry out fundamental science research in the Woolgar lab. The ideal post holder will have prior experience with acquisition of fMRI and/or MEG data
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and apply cutting-edge techniques in crystal synthesis, characterisation and modelling to study and generate a new understanding of MoSS. These advances will have applications across multiple sectors
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secondary datasets). Apply advanced analytical techniques (e.g., econometrics models; AI tools; MCDA; cognitive mapping) to generate robust, actionable insights. Design innovative financing channels and
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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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, environmental simulation and modelling to investigate the surfaces and atmospheres of the terrestrial planets, the Moon, asteroids, comets and extra-terrestrial materials. The post-holder will be expected to work