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Field
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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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Qualifications / Experience: • A PhD in Physics, Geoscience or a related field • Proven expertise in numerical modelling using super computing clusters • Excellent knowledge of atmospheric physics • Proficiency in
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associate will integrate and develop optimal transport theories and computational methods for the forward and inverse modeling in these applications. We are not accepting applications for this job through
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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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Postdoc: Geospatial AI Modelling & Uncertainty Quantification of Biomass Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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will develop deep learning algorithms and validate models on diverse clinical data which include magnetic resonance imaging (MRI) and computed tomography (CT). In this role the successful candidate will
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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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Postdoc: Hybrid Geospatial Modelling and Scenario Development of Biomass Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional
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We are seeking to appoint a Researcher in Infectious Disease Modelling. You will be an integral part of the Pathogen Dynamics group at Oxford, led by Christophe Fraser, where the team focuses