433 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" "UNIS" Postdoctoral positions in United Kingdom
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To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development
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Generative Modelling to apply and develop cutting-edge deep generative probabilistic models including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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Bioinformatics and Computational Biology headed by Ivo Hofacker. Our team works on the development of algorithms and methods for problems in Computational Chemistry, Systems Chemistry, and Computational Biology
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fingerprint identification (RFFI) for Wi-Fi. You will design novel RFFI algorithms and further evaluate their performance using practical testbeds such as software-defined radio platforms. You should have a PhD
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fingerprint identification (RFFI) for Wi-Fi. You will design novel RFFI algorithms and further evaluate their performance using practical testbeds such as software-defined radio platforms. You should have a PhD
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and method development, is highly beneficial. You will be expected to manage your own academic research and administrative activities. This involves project management, to co-ordinate multiple aspects
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currently leading the NeuroHand project—a UKRI-funded programme focused on the development of a soft robotic exoskeleton glove for stroke rehabilitation. The role provides an exciting opportunity
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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Professor Daniel Paulusma from Durham University and Professor Vadim Lozin from the University of Warwick. The general aim of the project is to develop algorithmic meta-classifications, which state that any