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- Prof. Ruilin Pei and Shenyang University of Technology
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, training deep learning models to adapt designs to boundary conditions, and integrating FEM workflows within parametric modeling environments like Grasshopper. The candidate will contribute to building a
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affecting the intestinal epithelium and the possible partners related to the different phases of the disease. The work will be a combination of ex vivo organoid cultures, in vivo disease models, combined with
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degree . In your EOI, nominate Associate Professor Angela Watson as your proposed principal supervisor, and copy the link to this scholarship website into question two of the financial details section
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Training: Conduct Silvaco TCAD simulations (fabrication processes, device modeling, and circuit-level simulations) . Experimental Work: Participate in cleanroom processes, device fabrication, and electrical
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-cycle fatigue. The research methods are based on both small-scale and full-scale experimental testing and on Finite Element Modelling. Are you motivated to take a step towards a doctorate and open
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in analytical theory from Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research
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collaborative efforts among researchers at the University of Utah and UC San Diego in developing and applying methods in predictive and causal modeling of complex biomedical and social processes and systems
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previous studies have yielded a wide array of conceptualizations and insights, research on this complex issue still suffers from a lack of sufficiently detailed conceptual models, from a paucity of insight
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, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have excellent communication skills and will to collaborate with researchers from different disciplines. You have
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diseases, and how these influence, or are influenced by, labor force participation and income. In addition, you will develop simulation models to predict how different policies could reduce the disease