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Associate with mathematical modelling and numerical/data analysis background to join our food system resilience project, led by University of Reading, joining a large interdisciplinary team with an excellent
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. About You The successful applicant will have, or soon obtain, a PhD degree in mathematics or related, or equivalent level of professional qualifications and experience, with expertise in at least one of
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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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. To address these questions, we combine a series of interdisciplinary approaches ranging from experimental embryology and fluorescent microscopy to mathematical modelling. The lab is highly interdisciplinary
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modelling the coupling of atmospheric and micro-physics moisture dynamics. The work will be carried out in collaboration with and under the supervision of Professor Edriss S. Titi. Duties include mathematical
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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models
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mathematical and computational techniques, it is essential to have experience in mathematical modelling / dynamical systems theory / numerical methods / coding. An ideal candidate would have a PhD, or
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skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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and Innovation Associate to join this ambitious project. You should hold a PhD in a relevant field such as applied/pure mathematics or physics and have an established track record of original research