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) that connects different countries with the overarching aim to develop cutting-edge human in vitro and in silico biomedical tools to better understand the biology of brain disease/disorders. You will employ
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both groundbreaking research and world-class education for undergraduate and graduate students. As a team, we represent different backgrounds, skills and views. We foster an inclusive culture, as our
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for admission to the faculty's Doctoral Programme . Good programming skills in one or several languages (e.g., Python, Julia, MATLAB, Fortran, C++). Competence in mathematical modeling and languages
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High Performance Computing facility, where the current code is implemented. The candidate will, among other activities, extend the model to treat different management interventions, peat growth and decay
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different rough surfaces, and if concepts derived from our understanding of equilibrium flows, such as the roughness function and the equivalent sand-grain roughness, still apply in any form under non
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. At the intersection of different sub-fields (applied mathematics, software engineering, and medicine), it is expected to refine the scope and goals of the project with the selected applicant, depending on technical
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are to: Develop a probabilistic machine learning tool that can determine the optimal grinding parameters for different scenarios based on required material removal depth and rail grade. Generate data through
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we have are inaccurate. By conducting experiments in Arctic weather over a longer period of time, at different geographic locations, we hope to build a time series of measurements suitable
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of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g. random forest (RF
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to address these combinatorial decision challenges, with a specific focus on nanocellulose production in the UK. By exploring different feedstock options, supply chain configurations, and process pathways