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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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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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want The postdoctoral research associate will be working on developing machine learning/artificial intelligence algorithms for various applications, including energy systems, health systems, and marine
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with both research and teaching responsibilities. Preference will be given to candidates whose expertise aligns and strengthens the research of the Department. The successful candidates must have a PhD
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, Python, and R. The candidate should have a strong capacity to understand processes underlying pro-environmental behaviour from different perspectives, enabling them to simultaneously understand, use, and
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machine learning are desirable, applicants from other quantitative fields (e.g. math, physics, statistics, computer science) who are eager to learn about neuroscience are highly encouraged to apply as well
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with society. Whether our contributions come in the form of excellent research, innovative solutions, education or learning, we must make a positive difference to society and contribute to a sustainable
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, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by developing theories, principles, tools and methods
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of CLiPS, which focuses on the application of statistical and machine learning methods, trained on corpus data, to explain human language acquisition and processing data, and to develop automatic text
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description