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the optimization-based methods (doi.org/10.1016/j.apenergy.2020.116152 ), 3- Weakness of the model-predictive-control (MPC) against HESS’s parameters uncertainties, noises, and disturbances (doi.org/10.2514/6.2022
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. For example, anaerobic bacteria culture and human cell co-culture infection models; cell viability assays and cancer invasion and migration assays; ELISA, quantitative PCR, DNA damage assays
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vehicles, data centers, etc.). These devices are mostly power electronic interfaced introducing new types of dynamic phenomena and the need for more detailed models, increasing complexity. In addition
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, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In
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, understand, characterise, and model the effect of surface roughness on wall turbulence when out of equilibrium. Particular emphasis will be made on determining if any form of flow similarity exists across
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modelling. The Hub provides cutting-edge laboratory infrastructure alongside SEMS's extensive facilities portfolio. You will work under the supervision of Dr. Salvatore Grasso (https://www.sems.qmul.ac.uk
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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grade. Generate data through comprehensive laboratory grinding tests on various rail grades to train and validate the ML model. Utilise numerical modelling to establish acceptable thresholds for surface
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environmentally viable at scale. Research Aims: Process Intensification & Scale-Up Design and evaluate scalable MEC architectures (cassette and containerised). Use CFD modelling and lab pilots to optimise
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: adapting existing models of bird movement and migration to test hypotheses about the ecological processes shaping observed connectivity patterns. Identify species and populations that have the highest