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Field
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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/SinglePageApplicationForm.aspx… Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Professional skills Experience in: Reinforcement Learning (RL), Model Predictive Control (MPC
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are supported with recommendations on how to best improve their sustainability achievements; Leading and working in project teams that vary in size and complexity. These may vary from about five colleagues
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models with a practical experimental platform. FTE: 1 (35 hours/week) Term: Fixed (18 months) The Centre for Ultrasonic Engineering (CUE) group of the Institute for Sensors, Signals and Communications
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therapeutics will be tested in these models, such as vagus nerve stimulation and anti-inflammatory compounds. Some of these scientific questions will further address the complexity of innate immune signaling
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. We are seeking a Research Scientist specializing in plasma physics to join our team. In this role, you will: Develop and refine models to understand and predict the behavior of plasmas near the wall
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About the Role Join a multi-disciplinary team that explores human intestinal development and disease using cutting edge single cell and spatial biology technologies, organoid models and
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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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the complexity of religious realities in Asia necessitates rethinking traditional Western-derived concepts of religion and their relationship to society. Historically, Asian religions have been interpreted either
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading