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computational analyses of epigenomic/transcriptomic data and machine learning. Experience in single-cell omics data is desirable. The post holder will be responsible to develop pipelines for the analysis
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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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vision research. The department fosters interdisciplinary collaboration, addressing real-world challenges through innovative machine learning, data science, and intelligent systems research. About the role
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sufficiently versatile to rapidly acquire the required capacity. You will have : A PhD in gut microbiology or closely related field Good knowledge of gut modelling and general microbiology Experience in
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-centred AI applications. This position is highly interdisciplinary as it supports the advancement of data visualisation, machine learning, and artificial intelligence domains. The Post-Doctoral Researcher
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: PhD or equivalent degree in Robotics, Computer Science, Machine Learning, AI, Control Engineering, or a related field. Excellent programming skills and experience with related tools and software. A
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the areas: AI, deep neural networks, machine learning, applied topology, probability, statistics, signal processing. About the School The School has an exceptionally strong research presence across
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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the area constraint programming, machine learning, and artificial intelligence within an established group of researchers and PhD students. The nature and structure of the Phase 2 Research Ireland Centre
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. Familiarity with standard design verification (DV) procedures and continuous integration (CI) setups would be beneficial. Knowledge of machine learning workloads and the design of machine-learning accelerators