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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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+to+apply#Howtoapply-Eligibility) a Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Cognitive Science, Psychology or a related field excellent knowledge in AI and at least one
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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- and time-specific innervation that extends into adolescence. Our lab has used whole-brain tissue clearing, light-sheet imaging, and machine learning to map the spatial and temporal dynamics of serotonin
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue
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together world-class expertise in textiles, materials, soft robotics, biomechanics, sports, healthcare, machine learning and AI, with globally leading industrial and academic partners. Your Project
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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge