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Internet Exchange (AMS-IX), and the Faculty of Science of the University of Amsterdam. About Research group The CWI Machine Learning research group focuses on how computer programs can learn from and
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in combination with other machine learning techniques, to create predictive models. You will engage in an interactive feedback loop with domain experts to analyze discovered models and remove any
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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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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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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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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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, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where applicable) how images interact with epigraphic frames and contexts of production and
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PhD degree in either machine learning or computational molecular sciences. Advanced knowledge in molecular machine learning. Advanced knowledge in computational chemistry. Advanced programming skills
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policies have to a large degree focussed on technology shifts to bring about the required emission reductions. However, next to technology, behavioural change is crucial to achieve climate goals. Social and