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. Skills Terms programming; R; Python; Statistical methods; machine learning; differential expression; bulk RNAseq; scRNAseq; Linux/unix; spatial transcriptomics URLs/references https://ramialison
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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computer programming background who has an interest in ecology and biodiversity conservation, or an ecologist with computational modelling experience (e.g., using R, Python, Matlab). Project funding
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). Available at SSRN: https://ssrn.com/abstract=3461418 or http://dx.doi.org/10.2139/ssrn.3461418 Fitzgibbon, L.J., D. L. Dowe and F. Vahid (2004). Minimum Message Length Autoregressive Model Order
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). Available at SSRN: https://ssrn.com/abstract=3461418 or http://dx.doi.org/10.2139/ssrn.3461418 Fitzgibbon, L.J., D. L. Dowe and F. Vahid (2004). Minimum Message Length Autoregressive Model Order
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knowledge Applicants should have: A good background in machine learning, deep learning, artificial intelligence, or computer vision Strong Python programming skills Experience with at least one deep
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background in machine learning, deep learning, artificial intelligence, or computer vision Strong Python programming skills Experience with at least one deep-learning framework, preferably PyTorch An