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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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mathematical and numerical tools? Are you an innovative and productive team player who enjoys working in an interdisciplinary team with a background ranging from mathematics and theoretical physics all the way
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the research team around Univ.-Prof. Dr. Philipp Grohs. Our ideal candidate has a strong interest in Applied Mathematics, possesses solid and profound background knowledge in Numerical Analysis, Approximation
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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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(preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models to the clinic The post holder will be based in the Department
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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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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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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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home of several third-party funded research projects. Through cooperation with numerous academic institutions worldwide, the Department is very well connected with the international scholarly community
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temporal properties: ultrabroadband supercontinua, intense sub-cycle field transients, and few-femtosecond ultraviolet pulses, among many others. We combine numerical modelling with experiments to study the