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Field
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memorisation capabilities of deep learning models. Such vulnerabilities expose FL systems to various privacy attacks, making the study of privacy in distributed settings increasingly complex and vital
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
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data analysis, programming, and biology. You will be part of a collaborative research team with deep experimental and analytical expertise, with access to advanced tumor models and state-of-the-art
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
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skillset, deep industry insights, and a commitment to sustainability, fully prepared to drive the decarbonisation of aviation in diverse careers spanning industry, academia, government, and policymaking
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. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based training programme with emphasis on innovation and impact, collaborative working and learning, continuous development
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centrifugal, digital, capillary, pressure, or microvalve-based microfluidics. Experience in deep-learning and artificial intelligence in the field of microfluidics to support applications such as high
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. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI