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to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models (possibly based on machine learning) can be
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-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models
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for improving a healthy lifespan. To reach this goal, we will use machine learning, sonification strategies, mathematical modelling, and experimental systems of cell biology and aging. AUTORHYTHM builds
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is to decipher the spatiotemporal nature of autophagy and make predictions for improving a healthy lifespan. To reach this goal, we will use machine learning, sonification strategies, mathematical
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fields both locally (field by field) and regionally (groups of fields). Data driven analyses will be complemented by physical reservoir modelling, with machine learning approaches to extract correlations
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previous experience in application of machine learning techniques within energy systems strong analytical skills and ability to collect, analyze and study the impact of low carbon technologies good knowledge
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machine learning techniques Interest or experience in statistical modelling and analysis Interest in mathematics and natural science Experience with scientific publication and conducting research Experience
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try again. 16th May 2024 Languages English English English PhD Research Fellow in Machine Learning Apply for this job See advertisement About the position PhD Research Fellow Position in Machine
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27th May 2024 Languages English English English PhD Research Fellow in machine-learning methods for education research Apply for this job See advertisement About the position A position in a
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part of a recently funded centre-of-excellence (INTEGREAT). INTEGREAT, which has a focus on knowledge driven machine-learning, is funded from the Research Council of Norway (RCN) for ten years, and will