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challenges. The successful candidate should have the following: Relevant PhD qualification Sustained record of impactful research which can contribute to REF 2029 and wider research agenda Growing trajectory
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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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an international reputation in AI and Machine Learning, Cyber Security, 5G and Next Generation Communication Systems and Advanced Materials Innovation. The teaching is organised at a School level and you will join
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tools. In this role, you will mainly focus on strengthening our computational pipeline: integrating multiple standalone machine‑learning predictors into a unified, multi‑objective framework capable
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real-world deployment scenarios and collaborate with global organizations to ensure practical implementation and scalability. Key Research Areas: Software engineering, AI/machine learning, IoT
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departments, and a thriving ecosystem of museums, libraries, and research centres. At Cambridge, our mission is to contribute to society through world-class education, learning, and research. With a deep
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an international reputation in AI and Machine Learning, Cyber Security, 5G and Next Generation Communication Systems and Advanced Materials Innovation. The teaching is organised at a School level and you will join
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are looking for an ambitious candidate with a strong background in mathematical and statistical methods for both physics-based modelling and machine learning, and their application to engineering problems in
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
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information and advice on best-practice methodologies in machine learning/deep learning. It is essential that you hold a PhD/DPhil (or close to completion) in a relevant quantitative field (e.g. biostatistics