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analysing medical images and working with data Strong programming skills in R or Python Good communication skills and ability to work in a team Ideally, you would also have: Experience with AI and machine
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execution. This work involves creating frameworks for adaptive decision-making, using techniques from operations research and machine learning. This particular thematic area will be supervised by Associate
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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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microbiology, and machine learning, you will identify AMR genes, pathogens of public health concern (including ESKAPE and WHO-priority organisms), and reconstruct metagenome-assembled genomes (MAGs). Across five
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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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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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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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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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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