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Field
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Bioinformatics, as well as the Departments of Biostatistics & Biomedical Engineering, University of Michigan is seeking a postdoctoral fellow for bioinformatics problems involving quantum machine learning and
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integration and AI models tailored for fish behaviour, health, and stress signal analysis. Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and
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We are seeking a Research Fellow to perform research on deployment of machine-learned models for health analytics on distributed IoT/edge/cloud systems using transprecise computing and contribute
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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successful in this role, we are looking for candidates to have the following skills & experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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in Artificial Intelligence (Machine Learning and Statistics) at CentraleSupélec, · Joël Eymery, Head of the Nanostructures and Synchrotron Radiation Team at CEA Grenoble, · Jean-Sébastien
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are demonstrated knowledge related to acoustic modelling, measurement and soundscape. o Essential are demonstrated data analytic skills, ideally with machine learning or statistical modelling • Other general
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research into planet formation/protoplanetary discs or the ISM/star formation and may also have some experience in statistical methods and/or machine learning. Dr Winter and QMUL are committed to improving
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Computer Science, Mathematics, Physics, Applied Economics, or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning frameworks. Proficiency in