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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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gyre (SPG) and Arctic and Antarctic sea ice cover, involving processes on smaller scales, which are often not well represented in modelling efforts focusing on the large scale tipping elements
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Genetics, Reproduction and Development (GRAD) Large Research Group to: Use single-cell omics techniques to analyze human pluripotent stem cells, human embryos, and stem cell-derived embryo models. Study how
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains
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numerical calculation skills and mathematical modelling skills Strong skills in solid state physics and quantum mechanics Experience in theoretical modelling and experimental investigation of optical devices
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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models
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(Kingdom of the) [map ] Subject Area: Advanced Nearest Neighbour Models for Active Matter Appl Deadline: 2025/08/10 11:59PM (posted 2025/07/14, listed until 2025/08/10) Position Description: Apply Position
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sustainable aviation fuel (SAF), and importantly, hydrogen. Plastics are comprised of numerous polymers, thus the products of each vary through chemical recycling processes This project seeks to develop an in