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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and
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of biosystems and for extracting knowledge from (vast) sets of biotech data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and
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related discipline. The candidates will have expertise in computational imaging, with: (i) an algorithmic focus, with particular interest in methods at the interface of deep learning and optimisation
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be the state estimation of the robotic system from external cameras. Familiarity with existing methods from these domains, such as Deep Learning, Quality-Diversity algorithms, reinforcement learning
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the possibility of yearly renewal subject to funding availability. Key Responsibilities • Conduct and lead research in 3D computer vision, deep learning, and AI for digital twin generation, publish findings in top
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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highly stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will