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machine learning. This particular thematic area will be supervised by Associate Professor Agni Orfanoudaki. You will be responsible for planning and managing your own research programme within
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engineering, or related disciplines who are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models
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. Experience in MS Office computer skills is essential to create, access and submit reports for review and approval. Further, the post holder may be required to take an active role in other projects within
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for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics
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data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data management and version control (Git/GitHub
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background in mathematics, statistics, population genetics, phylogenetics, epidemiological modelling, or machine learning. Highly motivated candidates with some, but not all, of the skills requested will be
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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research for understanding the learned algorithms in brains and machines. The post holder will provide guidance to less experienced members of the research group, including postdocs, research assistants