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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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swine ALS model. The postholder will lead wet-lab experimental work, across biophysical (MANT-ATP) and functional genomic assays (methylome, transcriptome and proteome), generating and integrating multi
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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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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform
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for this post. The successful candidate will be required to develop a personal research programme in theoretical cosmology (which may include numerical modelling and/or data analysis), interacting with faculty
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In Vitro Predictive Models to Explore Tendinopathy”. The project is funded by the Medical Research Council (MRC) and part of the organ-chip research work underway within the Centre for Predictive in
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strategies, discovered within our group, to combat heart failure. The work will integrate studies using isolated cell systems with advanced in vivo models, with a particular focus on characterising newly
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strategies, discovered within our group, to combat heart failure. The work will integrate studies using isolated cell systems with advanced in vivo models, with a particular focus on characterising newly
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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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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