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knowledge graph models of such transformations that are self-describing when applied to existing map repositories, and can be scaled up to large data repositories using state-of-the-art AI methods. In
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programming, statistics, machine learning and big data approaches in the context of soil-vegetation-atmosphere interactions excellent writing and oral communication skills in English and strong ambition
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(UQ) for machine learning and its validation. Your areas of research will be chosen based on both your own expert judgement and insight into trends and developments and on team requirements to ensure
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for this position will have the following qualifications/qualities A PhD degree in either machine learning or computational molecular sciences. Advanced knowledge in molecular machine learning. Advanced knowledge in
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expertise in data science, with hands-on experience in techniques such as machine learning, reinforcement learning, and simulations, and in handling large-scale, within-person, or real-time datasets; A strong
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, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML (Random Forest, SVM, Fully Connected Neural Networks) will be essential
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on methods such as functional connectivity analysis, brain network analysis, or machine learning; Excellent scientific writing and communication skills in English; Ability to work independently while
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bulk and clonal protein expression data from large melanoma cohorts, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques
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of making in ancient Roman visual culture (e.g. depictions of craftsmen at work, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where